Datasets:

hunoutl
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paperlists

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null	null	2018	null	null	null	null	null	null	null	null	null	task clustering;matrix completion;multi-task learning;few-shot learning	null	null	null	iclr	0	null	main	4.666667	4;5;5	null	null	Robust Task Clustering for Deep and Diverse Multi-Task and Few-Shot Learning	null	null	4	Withdraw	4;4;4	null
null	Department of Electronics and Computer Science, University of Southampton	2018	null	null	null	null	null	null	null	Yan Zhang, Jonathon Hare, Adam Prugel-Bennett	https://iclr.cc/virtual/2018/poster/307	visual question answering;vqa;counting	null	null	null	iclr	-1	null	main	5.333333	4;6;6	null	null	Learning to Count Objects in Natural Images for Visual Question Answering	https://github.com/Cyanogenoid/vqa-counting	null	3.333333	Poster	4;3;3	null
null	null	2018	null	null	null	null	null	null	null	null	null	null	null	null	null	iclr	0	null	main	0	null	null	null	THINK VISUALLY: QUESTION ANSWERING THROUGH VIRTUAL IMAGERY	null	null	0	Active	null	null
null	null	2018	null	null	null	null	null	null	null	null	null	interpretability;generative adversarial networks	null	null	null	iclr	-0.240192	null	main	6.333333	4;7;8	null	null	Thinking like a machine — generating visual rationales through latent space optimization	null	null	3	Reject	3;4;2	null
null	University of British Columbia	2018	null	null	null	null	null	null	null	Glen Berseth, Cheng Xie, Paul Cernek, Michiel van de Panne	https://iclr.cc/virtual/2018/poster/300	Reinforcement Learning;Distillation;Transfer Learning;Continual Learning	null	null	null	iclr	1	null	main	6.333333	5;7;7	null	null	Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control	null	null	3.666667	Poster	3;4;4	null
null	Carnegie Mellon University; Google Brain	2018	null	null	null	null	null	null	null	null	null	squad;stanford question answering dataset;reading comprehension;attention;text convolutions;question answering	null	null	null	iclr	0.654654	null	main	6.333333	5;6;8	null	null	QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension	null	null	4	Poster	4;3;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	Non-convex optimization;Deep Learning	null	null	null	iclr	-0.866025	null	main	5.333333	4;6;6	null	null	No Spurious Local Minima in a Two Hidden Unit ReLU Network	null	null	3	Workshop	4;3;2	null
null	null	2018	null	null	null	null	null	null	null	null	null	boosting learning;deep learning;neural network	null	null	null	iclr	-0.960769	null	main	4.333333	2;5;6	null	null	Deep Boosting of Diverse Experts	null	null	4	Reject	5;4;3	null
null	null	2018	null	null	null	null	null	null	null	null	null	reinforcement learning;safe exploration;dqn	null	null	null	iclr	-0.866025	null	main	5.666667	5;5;7	null	null	Avoiding Catastrophic States with Intrinsic Fear	null	null	4	Reject	4;5;3	null
null	Columbia University, New York, NY 10027, USA	2018	null	null	null	null	null	null	null	Christopher Cueva, Xue-Xin Wei	https://iclr.cc/virtual/2018/poster/245	recurrent neural network;grid cell;neural representation of space	null	null	null	iclr	0	null	main	8.333333	8;8;9	null	null	Emergence of grid-like representations by training recurrent neural networks to perform spatial localization	null	null	4	Poster	4;4;4	null
null	Microsoft Research Montreal; Montréal Institute for Learning Algorithms (MILA), Université de Montréal, CIFAR Senior Fellow; Montréal Institute for Learning Algorithms (MILA), Université de Montréal, Work done while author was an intern at Microsoft Research Montreal; Montréal Institute for Learning Algorithms (MILA), Ecole Polytechnique de Montréal	2018	null	null	null	null	null	null	null	Sandeep Subramanian, Adam Trischler, Yoshua Bengio, Christopher Pal	https://iclr.cc/virtual/2018/poster/99	distributed sentence representations;multi-task learning	null	null	null	iclr	0	null	main	6.666667	4;8;8	null	null	Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning	null	null	5	Poster	5;5;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	clustering;deep learning;neural networks	null	null	null	iclr	-0.5	null	main	2.666667	2;3;3	null	null	Clustering with Deep Learning: Taxonomy and New Methods	null	null	4.666667	Reject	5;5;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	NLU;word embeddings;representation learning	null	null	null	iclr	-1	null	main	5.666667	5;5;7	null	null	Learning to Compute Word Embeddings On the Fly	null	null	3.666667	Reject	4;4;3	null
null	null	2018	null	null	null	null	null	null	null	null	null	program synthesis;program induction;example selection	null	null	null	iclr	-0.5	null	main	4.666667	4;5;5	null	null	Learning to select examples for program synthesis	null	null	3.666667	Reject	4;3;4	null
null	null	2018	null	null	null	null	null	null	null	Anonymous	null	Deep Learning;Autoencoders;Alternating Optimization	null	null	null	iclr	0.755929	null	main	5.666667	4;6;7	null	null	Training Autoencoders by Alternating Minimization	null	null	4.333333	Reject	4;4;5	null
null	UC Berkeley, Department of Electrical Engineering and Computer Science	2018	null	null	null	null	null	null	null	Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel	https://iclr.cc/virtual/2018/poster/64	meta-learning;few-shot learning	null	null	null	iclr	1	null	main	6.333333	6;6;7	null	null	A Simple Neural Attentive Meta-Learner	null	null	3.333333	Poster	3;3;4	null
null	Google Brain	2018	null	null	null	null	null	null	null	Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein	https://iclr.cc/virtual/2018/poster/91	Gaussian process;Bayesian regression;deep networks;kernel methods	null	null	null	iclr	-0.755929	null	main	5.666667	4;6;7	null	null	Deep Neural Networks as Gaussian Processes	null	null	3.666667	Poster	4;4;3	null
null	null	2018	null	null	null	null	null	null	null	null	null	Deep Reinforcement Learning;mult-agent systems	null	null	null	iclr	0	null	main	3.333333	3;3;4	null	null	Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning	null	null	4	Reject	5;3;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	null	null	null	null	iclr	0.5	null	main	5.666667	5;5;7	null	null	Federated Learning: Strategies for Improving Communication Efficiency	null	null	4.333333	Reject	3;5;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	SVM;siamese network;one-shot learning;few-shot learning	null	null	null	iclr	0	null	main	4	3;4;5	null	null	Make SVM great again with Siamese kernel for few-shot learning	null	null	4.333333	Reject	4;5;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	autonomous lane changing;decision making;deep reinforcement learning;q-learning	null	null	null	iclr	0	null	main	3	3;3;3	null	null	Tactical Decision Making for Lane Changing with Deep Reinforcement Learning	null	null	4.666667	Withdraw	5;4;5	null
null	The University of Tokyo, RIKEN; The University of Tokyo	2018	null	null	null	null	null	null	null	Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada	https://iclr.cc/virtual/2018/poster/259	sound recognition;supervised learning;feature learning	null	null	null	iclr	0	null	main	7	4;8;9	null	null	Learning from Between-class Examples for Deep Sound Recognition	https://github.com/mil-tokyo/bc_learning_sound/	null	4	Poster	4;4;4	null
null	Redwood Center for Theoretical Neuroscience, University of California, Berkeley	2018	null	null	null	null	null	null	null	Alexander Anderson, Cory P Berg	https://iclr.cc/virtual/2018/poster/244	Binary Neural Networks;Neural Network Visualization	null	null	null	iclr	1	null	main	6	4;7;7	null	null	The High-Dimensional Geometry of Binary Neural Networks	null	null	3.666667	Poster	3;4;4	null
null	Department of Applied Mathematics and Statistics, Johns Hopkins University; Department of Computer Science, Johns Hopkins University	2018	null	null	null	null	null	null	null	Raman Arora, Amitabh Basu, Poorya Mianjy, Anirbit Mukherjee	https://iclr.cc/virtual/2018/poster/155	expressive power;benefits of depth;empirical risk minimization;global optimality;computational hardness;combinatorial optimization	null	null	null	iclr	-0.5	null	main	6.333333	6;6;7	null	null	Understanding Deep Neural Networks with Rectified Linear Units	null	null	4.333333	Poster	4;5;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	Deep Reinforcement Learning;Multi-Agent Reinforcement Learning;StarCraft Micromanagement Tasks	null	null	null	iclr	0.866025	null	main	4.666667	4;5;5	null	null	Revisiting The Master-Slave Architecture In Multi-Agent Deep Reinforcement Learning	null	null	4	Reject	3;4;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	Deep learning;Structured Prediction;Natural Language Processing;Neural Program Synthesis	null	null	null	iclr	0	null	main	5.333333	4;5;7	null	null	Neural Program Search: Solving Data Processing Tasks from Description and Examples	null	null	4	Workshop	4;4;4	null
null	Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA	2018	null	null	null	null	null	null	null	Abram Friesen, Pedro Domingos	https://iclr.cc/virtual/2018/poster/92	hard-threshold units;combinatorial optimization;target propagation;straight-through estimation;quantization	null	null	null	iclr	0	null	main	7	7;7;7	null	null	Deep Learning as a Mixed Convex-Combinatorial Optimization Problem	null	null	3.666667	Poster	3;4;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	domain adaptation;neural networks;generative models;discriminative models	null	null	null	iclr	-1	null	main	5.333333	5;5;6	null	null	Principled Hybrids of Generative and Discriminative Domain Adaptation	null	null	3.666667	Reject	4;4;3	null
null	null	2018	null	null	null	null	null	null	null	null	null	style transfer;text generation;non-parallel data	null	null	null	iclr	0	null	main	0	null	null	null	Language Style Transfer from Non-Parallel Text with Arbitrary Styles	null	null	0	Withdraw	null	null
null	null	2018	null	null	null	null	null	null	null	null	null	deep reinforcement learning;task execution;instruction execution	null	null	null	iclr	-0.5	https://youtu.be/e_ZXVS5VutM	main	5.333333	4;6;6	null	null	Neural Task Graph Execution	null	null	3.666667	Reject	4;3;4	null
null	Preferred Networks, Inc.; Ritsumeikan University; National Institute of Informatics	2018	null	null	null	null	null	null	null	Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida	https://iclr.cc/virtual/2018/poster/331	Generative Adversarial Networks;Deep Generative Models;Unsupervised Learning	null	null	null	iclr	0	null	main	7.333333	7;7;8	null	null	Spectral Normalization for Generative Adversarial Networks	https://github.com/pfnet-research/sngan_projection	null	3	Oral	4;2;3	null
null	Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583	2018	null	null	null	null	null	null	null	Pan Zhou, Jiashi Feng, Pan Zhou	https://iclr.cc/virtual/2018/poster/329	Deep Learning Analysis;Deep Learning Theory;Empirical Risk;Landscape Analysis;Nonconvex Optimization	null	null	null	iclr	0	null	main	5.666667	3;7;7	null	null	Empirical Risk Landscape Analysis for Understanding Deep Neural Networks	null	null	3	Poster	3;3;3	null
null	MPI for Intelligent Systems; University of Amsterdam; Google Brain	2018	null	null	null	null	null	null	null	Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schoelkopf	https://iclr.cc/virtual/2018/poster/76	fidelity-weighted learning;semisupervised learning;weakly-labeled data;teacher-student	null	null	null	iclr	0	null	main	6	5;6;7	null	null	Fidelity-Weighted Learning	null	null	3.666667	Poster	4;3;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	Distributional shift;causal effects;domain adaptation	null	null	null	iclr	0.188982	null	main	6.666667	5;7;8	null	null	Learning Weighted Representations for Generalization Across Designs	null	null	3.333333	Reject	3;4;3	null
null	Google Brain	2018	null	null	null	null	null	null	null	Samuel Smith, Pieter-Jan Kindermans, Chris Ying, Quoc V Le	https://iclr.cc/virtual/2018/poster/272	batch size;learning rate;simulated annealing;large batch training;scaling rules;stochastic gradient descent;sgd;imagenet;optimization	null	null	null	iclr	0	null	main	6.333333	6;6;7	null	null	Don't Decay the Learning Rate, Increase the Batch Size	null	null	4	Poster	4;4;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	Bayesian Deep Learning;Amortized Inference;Variational Auto-Encoders;Learning to Learn	null	null	null	iclr	1	null	main	5.333333	5;5;6	null	null	Learning to Infer	null	null	4.333333	Workshop	4;4;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	GAN;medical;records;time;series;generation;privacy	null	null	null	iclr	0	null	main	5	4;5;6	null	null	Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs	null	null	4	Reject	4;4;4	null
null	null	2018	null	null	null	null	null	null	null	Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook, Debbie Marr	https://iclr.cc/virtual/2018/poster/208	Low precision;binary;ternary;4-bits networks	null	null	null	iclr	0.5	null	main	6.333333	5;5;9	null	null	WRPN: Wide Reduced-Precision Networks	null	null	3.666667	Poster	4;3;4	null
null	Accelerator Architecture Lab, Intel Labs	2018	null	null	null	null	null	null	null	Asit Mishra, Debbie Marr	https://iclr.cc/virtual/2018/poster/173	Ternary;4-bits;low precision;knowledge distillation;knowledge transfer;model compression	null	null	null	iclr	0	null	main	7.333333	7;7;8	null	null	Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy	null	null	4	Poster	4;4;4	null
null	null	2018	null	null	null	null	null	null	null	Xu He, Herbert Jaeger	https://iclr.cc/virtual/2018/poster/233	Catastrophic Interference;Conceptor;Backpropagation;Continual Learning;Lifelong Learning	null	null	null	iclr	0	null	main	7	7;7;7	null	null	Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation	null	null	3.666667	Poster	3;3;5	null
null	Paper under double-blind review	2018	null	null	null	null	null	null	null	null	null	Reading Comprehension;Answering Multiple Choice Questions	null	null	null	iclr	-1	null	main	4.666667	4;5;5	null	null	ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions	null	null	3.333333	Reject	4;3;3	null
null	Princeton University; Columbia University	2018	null	null	null	null	null	null	null	Sanjeev Arora, Mikhail Khodak, Nikunj Umesh Saunshi, Kiran Vodrahalli	https://iclr.cc/virtual/2018/poster/96	theory;LSTM;unsupervised learning;word embeddings;compressed sensing;sparse recovery;document representation;text classification	null	null	null	iclr	-0.755929	null	main	6.666667	6;7;7	null	null	A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs	null	null	2.666667	Poster	4;3;1	null
null	The University of Melbourne, Parkville, Australia; National Institute of Informatics, Tokyo, Japan; University of Michigan, Ann Arbor, USA; Tsinghua University, Beijing, China; University of California, Berkeley, USA	2018	null	null	null	null	null	null	null	Xingjun Ma, Bo Li, Yisen Wang, Sarah Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, James Bailey	https://iclr.cc/virtual/2018/poster/328	Adversarial Subspace;Local Intrinsic Dimensionality;Deep Neural Networks	null	null	null	iclr	0.654654	null	main	7	6;7;8	null	null	Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality	null	null	2.666667	Oral	1;4;3	null
null	Department of Computer Science, University of Bonn, Germany; Fraunhofer Institute IAIS, Sankt Augustin, Germany	2018	null	null	null	null	null	null	null	Henning Petzka, Asja Fischer, Denis Lukovnikov	https://iclr.cc/virtual/2018/poster/17	null	null	null	null	iclr	0.866025	null	main	5	2;6;7	null	null	On the regularization of Wasserstein GANs	null	null	3.666667	Poster	2;5;4	null
null	Robotics Institute, Carnegie Mellon University; Volvo Construction Equipment, Volvo Group	2018	null	null	null	null	null	null	null	Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M Kitani	https://iclr.cc/virtual/2018/poster/132	Deep learning;Neural networks;Model compression	null	null	null	iclr	0	null	main	6	4;5;9	null	null	N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning	null	null	4	Poster	4;4;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	inversion scheme;deep neural networks;semi-supervised learning;MNIST;SVHN;CIFAR10	null	null	null	iclr	-1	null	main	5.333333	4;5;7	null	null	Semi-Supervised Learning via New Deep Network Inversion	null	null	3.666667	Reject	5;4;2	null
null	Courant Institute of Mathematical Sciences, Center for Data Science, New York, NY 10012, USA; Courant Institute of Mathematical Sciences, Center for Data Science, New York University, New York, NY 10012, USA	2018	null	null	null	null	null	null	null	Alex Nowak, David Folqué Garcia, Joan Bruna	https://iclr.cc/virtual/2018/poster/44	Neural Networks;Combinatorial Optimization;Algorithms	null	null	null	iclr	0	null	main	6.666667	6;7;7	null	null	Divide and Conquer Networks	null	null	3	Poster	3;3;3	null
null	null	2018	null	null	null	null	null	null	null	null	null	Word Embeddings;Tensor Factorization;Natural Language Processing	null	null	null	iclr	0	null	main	5	5;5;5	null	null	LEARNING SEMANTIC WORD RESPRESENTATIONS VIA TENSOR FACTORIZATION	null	null	4.333333	Reject	3;5;5	null
null	†The University of Hong Kong; ‡Salesforce Research	2018	null	null	null	null	null	null	null	Jiatao Gu, James Bradbury, Caiming Xiong, Victor OK Li, richard socher	https://iclr.cc/virtual/2018/poster/241	machine translation;non-autoregressive;transformer;fertility;nmt	null	null	null	iclr	0	null	main	6.666667	6;7;7	null	null	Non-Autoregressive Neural Machine Translation	null	null	4	Poster	4;4;4	null
null	UC Irvine; Amazon.com; Snap Inc; UC Santa Barbara; Think Big Analytics	2018	null	null	null	null	null	null	null	null	null	Recommender systems;deep learning;personalization	null	null	null	iclr	-0.5	null	main	6.333333	6;6;7	null	null	THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS	null	null	3.333333	Workshop	3;4;3	null
null	N/A	2018	null	null	null	null	null	null	null	null	null	vocabulary-informed learning;data augmentation	null	null	null	iclr	0.866025	null	main	5	4;5;6	null	null	VOCABULARY-INFORMED VISUAL FEATURE AUGMENTATION FOR ONE-SHOT LEARNING	null	null	3.666667	Reject	3;4;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	structured prediction;RAML;theory;Bayes decision rule;reward function	null	null	null	iclr	0	null	main	5.333333	5;5;6	null	null	Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML	null	null	3	Reject	4;2;3	null
null	The Institute for Theoretical Computer Science, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China; Department of Computer Science, University of Southern California, Los Angeles, USA	2018	null	null	null	null	null	null	null	Jiayuan Mao, Honghua Dong, Joseph J Lim	https://iclr.cc/virtual/2018/poster/3	reinforcement learning;transfer learning	null	null	null	iclr	1	null	main	6.333333	6;6;7	null	null	Universal Agent for Disentangling Environments and Tasks	null	null	3.333333	Poster	3;3;4	null
null	Stanford University; Google AI Perception; Google Brain	2018	null	null	null	null	null	null	null	Daniel Levy, Matthew D Hoffman, Jascha Sohl-Dickstein	https://iclr.cc/virtual/2018/poster/284	markov;chain;monte;carlo;sampling;posterior;deep;learning;hamiltonian;mcmc	null	null	null	iclr	-0.5	null	main	7	6;7;8	null	null	Generalizing Hamiltonian Monte Carlo with Neural Networks	https://github.com/google-research/google-research/tree/master/generalizing_hmc	null	3	Poster	3;4;2	null
null	null	2018	null	null	null	null	null	null	null	null	null	Reinforcement learning	null	null	null	iclr	1	null	main	5.666667	5;6;6	null	null	Learning Gaussian Policies from Smoothed Action Value Functions	null	null	3.666667	Reject	3;4;4	null
null	Department of Computer Science and Engineering, Indian Institute of Technology, Madras; Department of Mechanical Engineering, Indian Institute of Technology, Madras; Department of Electrical Engineering, Indian Institute of Technology, Madras; Department of Computer Science and Engineering, and Robert Bosch Centre for Data Science and AI (RBC-DSAI), Indian Institute of Technology, Madras	2018	null	null	null	null	null	null	null	Sahil Sharma, Ashutosh Kumar Jha, Parikshit Hegde, Balaraman Ravindran	https://iclr.cc/virtual/2018/poster/257	Deep Reinforcement Learning	null	null	null	iclr	0.5	null	main	6.333333	5;7;7	null	null	Learning to Multi-Task by Active Sampling	null	null	3.666667	Poster	3;3;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	forward modeling;partially observable;deep learning;strategy game;real-time strategy	null	null	null	iclr	0.944911	null	main	4.666667	4;5;5	null	null	Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger	null	null	2.666667	Reject	1;3;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	label noise;weakly supervised learning;robustness of neural networks;deep learning;large datasets	null	null	null	iclr	-0.5	null	main	4.666667	4;5;5	null	null	Deep Learning is Robust to Massive Label Noise	null	null	4.666667	Reject	5;4;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	Deep Learning;Deconvolutional Layer;Pixel CNN	null	null	null	iclr	-0.5	null	main	5.333333	5;5;6	null	null	Pixel Deconvolutional Networks	null	null	4.333333	Reject	5;4;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	Word embedding;tensor decomposition	null	null	null	iclr	0	null	main	5	5;5;5	null	null	Learning Covariate-Specific Embeddings with Tensor Decompositions	null	null	4	Reject	4;3;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	LVCSR;speech recognition;embedded;low rank factorization;RNN;GRU;trace norm	null	null	null	iclr	0.5	null	main	4.666667	4;5;5	null	null	Trace norm regularization and faster inference for embedded speech recognition RNNs	https://github.com/paddlepaddle/farm	null	3.666667	Reject	3;3;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	adversarial attacks;security;auto-encoder	null	null	null	iclr	-0.866025	null	main	4	3;4;5	null	null	LatentPoison -- Adversarial Attacks On The Latent Space	null	null	3.666667	Reject	4;4;3	null
null	null	2018	null	null	null	null	null	null	null	null	null	natural language processing;background knowledge;word embeddings;question answering;natural language inference	null	null	null	iclr	0.5	null	main	5.333333	5;5;6	null	null	Dynamic Integration of Background Knowledge in Neural NLU Systems	null	null	3.666667	Reject	4;3;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	Manifold Learning;Non-linear Dimensionality Reduction;Neural Networks;Unsupervised Learning	null	null	null	iclr	0.866025	null	main	4	3;4;5	null	null	Parametric Manifold Learning Via Sparse Multidimensional Scaling	null	null	4.333333	Reject	4;4;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	null	null	null	null	iclr	-0.5	null	main	4.666667	4;5;5	null	null	Attribute-aware Collaborative Filtering: Survey and Classification	null	null	4.666667	Withdraw	5;4;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	price predictions;expert system;recurrent neural networks;deep learning;natural language processing	null	null	null	iclr	1	null	main	4.666667	4;4;6	null	null	Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network	null	null	4.333333	Reject	4;4;5	null
null	Universit´e de Bretagne Sud, IRISA, UMR 6074, CNRS; Kyoto University, Graduate School of Informatics; NTT Communication Science Laboratories; Universit´e Cˆote d'Azur, Lagrange, UMR 7293, CNRS, OCA	2018	null	null	null	null	null	null	null	Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel	https://iclr.cc/virtual/2018/poster/179	optimal transport;Wasserstein;domain adaptation;generative models;Monge map;optimal mapping	null	null	null	iclr	0	null	main	6.75	6;6;7;8	null	null	Large Scale Optimal Transport and Mapping Estimation	null	null	3	Poster	3;3;3;3	null
null	Google	2018	null	null	null	null	null	null	null	H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang	https://iclr.cc/virtual/2018/poster/187	differential privacy;LSTMs;language models;privacy	null	null	null	iclr	0.5	null	main	7.333333	7;7;8	null	null	Learning Differentially Private Recurrent Language Models	null	null	3.333333	Poster	2;4;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	null	null	null	null	iclr	-0.5	null	main	4.666667	4;5;5	null	null	Learning what to learn in a neural program	null	null	3.333333	Reject	4;4;2	null
null	null	2018	null	null	null	null	null	null	null	null	null	null	null	null	null	iclr	0	null	main	5	4;5;6	null	null	Multimodal Sentiment Analysis To Explore the Structure of Emotions	null	null	5	Reject	5;5;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	RNNs	null	null	null	iclr	-0.188982	null	main	4.333333	3;4;6	null	null	Efficiently applying attention to sequential data with the Recurrent Discounted Attention unit	null	null	4.333333	Reject	4;5;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	Reinforcement Learning;Imitation Learning	null	null	null	iclr	-0.866025	null	main	5.666667	5;6;6	null	null	Faster Reinforcement Learning with Expert State Sequences	null	null	4	Reject	5;4;3	null
null	Department of Engineering Science, University of Oxford; Department of Statistics, University of Oxford	2018	null	null	null	null	null	null	null	Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood	https://iclr.cc/virtual/2018/poster/31	Variational Autoencoders;Inference amortization;Model learning;Sequential Monte Carlo;ELBOs	null	null	null	iclr	0.866025	null	main	5.666667	3;7;7	null	null	Auto-Encoding Sequential Monte Carlo	null	null	3	Poster	2;4;3	null
null	null	2018	null	null	null	null	null	null	null	null	null	supervised learning;unsupervised learning;self-organization;internal representation;topological structure	null	null	null	iclr	-1	null	main	2.666667	2;2;4	null	null	Self-Organization adds application robustness to deep learners	null	null	4.666667	Withdraw	5;5;4	null
null	Computer Science and Artiﬁcial Intelligence Laboratory, Massachusetts Institute of Technology; Paul G. Allen School of Computer Science & Engineering, University of Washington	2018	null	null	null	null	null	null	null	Yonatan Belinkov, Yonatan Bisk	https://iclr.cc/virtual/2018/poster/172	neural machine translation;characters;noise;adversarial examples;robust training	null	null	null	iclr	0	null	main	7.333333	7;7;8	null	null	Synthetic and Natural Noise Both Break Neural Machine Translation	null	null	4	Oral	4;4;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	Deep Reinforcement Learning;Domain Adaptation;Adversarial Networks	null	null	null	iclr	0.5	null	main	3	2;3;4	null	null	Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games	null	null	4	Reject	4;3;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	linear quadratic regulator;policy gradient;natural gradient;reinforcement learning;non-convex optimization	null	null	null	iclr	1	null	main	5.333333	5;5;6	null	null	Global Convergence of Policy Gradient Methods for Linearized Control Problems	null	null	3.333333	Reject	3;3;4	null
null	Under double-blind review	2018	null	null	null	null	null	null	null	null	null	adversarial examples	null	null	null	iclr	-0.944911	https://youtu.be/YXy6oX1iNoA	main	6.333333	5;6;8	null	null	Synthesizing Robust Adversarial Examples	null	null	3.666667	Reject	4;4;3	null
null	Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA	2018	null	null	null	null	null	null	null	Murat Kocaoglu, Christopher Snyder, Alexandros Dimakis, Sriram Vishwanath	https://iclr.cc/virtual/2018/poster/159	causality;structural causal models;GANs;conditional GANs;BEGAN;adversarial training	null	null	null	iclr	0	null	main	7.333333	6;7;9	null	null	CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training	null	null	3	Poster	3;3;3	null
null	Racah Institute of Physics, The Hebrew University of Jerusalem; Department of Engineering Science, University of Oxford; Assistant Professor at the Federal University of Rio Grande, Rio Grande, Brazil	2018	null	null	null	null	null	null	null	Zohar Ringel, Rodrigo Andrade de Bem	https://iclr.cc/virtual/2018/poster/303	Deep Convolutional Networks;Loss function landscape;Graph Structured Data;Training Complexity;Theory of deep learning;Percolation theory;Anderson Localization	null	null	null	iclr	1	null	main	6.666667	6;7;7	null	null	Critical Percolation as a Framework to Analyze the Training of Deep Networks	null	null	2.333333	Poster	1;3;3	null
null	Microsoft Business AI and Research, National Taiwan University; Microsoft Business AI and Research	2018	null	null	null	null	null	null	null	Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen, Weizhu Chen	https://iclr.cc/virtual/2018/poster/246	Attention Mechanism;Machine Comprehension;Natural Language Processing;Deep Learning	null	null	null	iclr	-0.866025	null	main	7.333333	7;7;8	null	null	FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension	null	null	4	Poster	5;4;3	null
null	Department of Computer Science and Operations Research, University of Montréal, Canada; Department of Computer Science, Stanford University, USA	2018	null	null	null	null	null	null	null	null	null	representation learning;auto-encoders;3D point clouds;generative models;GANs;Gaussian Mixture Models	null	null	null	iclr	0.755929	null	main	6.333333	5;6;8	null	null	Learning Representations and Generative Models for 3D Point Clouds	null	null	4.666667	Workshop	4;5;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	VAE;Generative Model;Vision;Natural Language	null	null	null	iclr	0.5	null	main	4.666667	4;5;5	null	null	Generative Entity Networks: Disentangling Entitites and Attributes in Visual Scenes using Partial Natural Language Descriptions	null	null	4.333333	Reject	4;4;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	learning from demonstration;reinforcement learning;maximum entropy learning	null	null	null	iclr	0.866025	null	main	5.333333	5;5;6	null	null	Reinforcement Learning from Imperfect Demonstrations	null	null	4	Workshop	3;4;5	null
null	Washington State University, Pullman; NEC Laboratories America	2018	null	null	null	null	null	null	null	Bo Zong, Qi Song, Martin Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen	https://iclr.cc/virtual/2018/poster/126	Density estimation;unsupervised anomaly detection;high-dimensional data;Deep autoencoder;Gaussian mixture modeling;latent low-dimensional space	null	null	null	iclr	0	null	main	8	8;8;8	null	null	Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection	null	null	4.333333	Poster	5;4;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	deep neural networks;short text classification;cybersecurity;domain generation algorithms;malicious domain names	null	null	null	iclr	0.188982	null	main	5.333333	4;5;7	null	null	Character Level Based Detection of DGA Domain Names	null	null	3.666667	Reject	4;3;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	hierarchical;tree-lstm;treelstm;syntax;composition	null	null	null	iclr	0	null	main	5	4;5;6	null	null	Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs	null	null	4	Reject	4;4;4	null
null	null	2018	null	null	null	null	null	null	null	Xu Chen, Jiang Wang, Hao Ge	https://iclr.cc/virtual/2018/poster/273	GAN;Primal-Dual Subgradient;Mode Collapse;Saddle Point	null	null	null	iclr	-0.5	null	main	6.666667	6;7;7	null	null	TRAINING GENERATIVE ADVERSARIAL NETWORKS VIA PRIMAL-DUAL SUBGRADIENT METHODS: A LAGRANGIAN PERSPECTIVE ON GAN	null	null	3.666667	Poster	4;3;4	null
null	Simon Fraser University, Burnaby, BC, Canada; Microsoft Research, Cambridge, UK	2018	null	null	null	null	null	null	null	Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi	https://iclr.cc/virtual/2018/poster/216	programs;source code;graph neural networks	null	null	null	iclr	0	null	main	8	8;8;8	null	null	Learning to Represent Programs with Graphs	null	null	4	Oral	4;4;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	optimization;vanishing gradients;shattered gradients;skip-connections	null	null	null	iclr	0	null	main	6	6;6;6	null	null	Avoiding degradation in deep feed-forward networks by phasing out skip-connections	null	null	4.333333	Reject	4;5;4	null
null	Carnegie Mellon University; DeepMind	2018	null	null	null	null	null	null	null	Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, Koray Kavukcuoglu	https://iclr.cc/virtual/2018/poster/45	deep learning;architecture search	null	null	null	iclr	0.5	null	main	6.666667	6;6;8	null	null	Hierarchical Representations for Efficient Architecture Search	null	null	3.666667	Poster	3;4;4	null
null	The University of Tokyo	2018	null	null	null	null	null	null	null	Raphael Shu, Hideki Nakayama	https://iclr.cc/virtual/2018/poster/242	natural language processing;word embedding;compression;deep learning	null	null	null	iclr	0	null	main	7	6;7;8	null	null	Compressing Word Embeddings via Deep Compositional Code Learning	null	null	4	Poster	4;4;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	null	null	null	null	iclr	0.866025	null	main	4.666667	4;4;6	null	null	The Context-Aware Learner	null	null	4	Reject	4;3;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	Few-Shot Learning;Neural Network Understanding;Visual Concepts	null	null	null	iclr	-0.755929	null	main	5.333333	4;5;7	null	null	Unleashing the Potential of CNNs for Interpretable Few-Shot Learning	null	null	4.333333	Reject	5;4;4	null
null	null	2018	null	null	null	null	null	null	null	Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L Griffiths	https://iclr.cc/virtual/2018/poster/313	meta-learning;learning to learn;hierarchical Bayes;approximate Bayesian methods	null	null	null	iclr	0	null	main	6.666667	6;7;7	null	null	Recasting Gradient-Based Meta-Learning as Hierarchical Bayes	null	null	3	Poster	3;3;3	null
null	null	2018	null	null	null	null	null	null	null	Guillaume Bellec, David Kappel, Wolfgang Maass, Robert Legenstein	https://iclr.cc/virtual/2018/poster/291	deep learning;pruning;LSTM;convolutional networks;recurrent neural network;sparse networks;neuromorphic hardware;energy efficient computing;low memory hardware;stochastic differential equation;fokker-planck equation	null	null	null	iclr	-0.755929	null	main	6.333333	5;6;8	null	null	Deep Rewiring: Training very sparse deep networks	null	null	4.333333	Poster	5;4;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	3D fMRI data;Deep Learning;Generative Adversarial Network;Classification	null	null	null	iclr	0.981981	null	main	6.333333	5;6;8	null	null	Hallucinating brains with artificial brains	null	null	4	Reject	3;4;5	null
null	null	2018	null	null	null	null	null	null	null	null	null	graph topology;GAN;network science;hierarchical learning	null	null	null	iclr	0.5	null	main	3.666667	3;4;4	null	null	Graph Topological Features via GAN	null	null	4.333333	Reject	4;5;4	null
null	null	2018	null	null	null	null	null	null	null	Sanjeev Arora, Andrej Risteski, Yi Zhang	https://iclr.cc/virtual/2018/poster/72	Generative Adversarial Networks;mode collapse;birthday paradox;support size estimation	null	null	null	iclr	-0.5	null	main	6.666667	6;7;7	null	null	Do GANs learn the distribution? Some Theory and Empirics	null	null	3.666667	Poster	4;3;4	null
null	null	2018	null	null	null	null	null	null	null	null	null	Machine learning;Neural networks;Sparse neural networks;Pre-defined sparsity;Scatter;Connectivity patterns;Adjacency matrix;Parameter Reduction;Morse code	null	null	null	iclr	0	null	main	4.333333	4;4;5	null	null	Characterizing Sparse Connectivity Patterns in Neural Networks	null	null	3	Reject	3;3;3	null

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paperlists Dataset

Dataset Details

This dataset contains the list of scientific papers accepted at one of these conferences:

CVPR (Computer Vision and Pattern Recognition)
ICCV (International Conference on Computer Vision)
ECCV (European Conference on Computer Vision)
CoRL (Conference on Robot Learning)
ICLR (International Conference on Learning Representations)
ICML (International Conference on Machine Learning)
NeurIPS (Conference on Neural Information Processing Systems)
SIGGRAPH (Special Interest Group on Graphics and Interactive Techniques)
EMNLP (Conference on Empirical Methods in Natural Language Processing)

From 2006 to the present (depending on the conferences)

Dataset Sources

The data comes from the paperlists repository

Uses

This dataset can be used to produce statistics on papers accepted at TOP-tier AI conferences

Data Collection and Processing

Please note, this is a raw dataset that has undergone little transformation. All fields are original except youtube which was merged into video and id, psid and ssid* which were deleted

Bias, Risks, and Limitations

The data is limited both by conference type and by year They are raw and have a lot of unspecified value

Citation

Paper Copilot

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16.2 MB

Size of the auto-converted Parquet files:

16.2 MB

Number of rows:

88,224