← Authors

Yoshua Bengio

293 papers · 2003–2026 · 18 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (18) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐝 Cross-Pollinator (13) 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (22) πŸ—ΊοΈ Taxonomy Completionist (44) 🌈 Renaissance Researcher (8) 🌟 Keyword Trendsetter Combo (6) 🏠 Conference Loyalist (78) πŸ“› The Namer πŸ‘‘ Domain Dominant (40) πŸ† Keyword Champion πŸ‘‘ Triple Crown 🌱 Topic Pioneer πŸ”¬ Deep Specialist (13) 🀝 Dynamic Duo (25) πŸ† Grand Slam πŸ‘₯ Mega-Team (39) ⚑ Prolific Year (29) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (17) ❓ The Questioner (8) πŸ’Ž Century Club (292) πŸ—ƒοΈ Keyword Collector (224)

Conferences

NIPS (78) ICLR (73) ICML (52) ACL (15) JMLR (15) AISTATS (14) AAAI (13) INTERSPEECH (8) UAI (7) EMNLP (4) ICCV (3) NAACL (3) EACL (2) IJCNLP (2) ECCV (1) CVPR (1) IJCAI (1) CLEAR (1)

Top co-authors

Anirudh Goyal (25) Aaron Courville (25) Nikolay Malkin (25) Dinghuai Zhang (20) Moksh Jain (14) Nan Rosemary Ke (13) Emmanuel Bengio (13) Kyunghyun Cho (12) Tristan Deleu (11) Alex Lamb (11)

Research topics

Architectures (1) Linguistics (1) Optimization & Theory (1)

Keywords

representation learning (27) neural network (23) recurrent neural network (19) variational inference (16) generative model (15) generative flow network (12) deep learning (11) feature learning (9) semi-supervised learning (9) unsupervised learning (9) generative adversarial network (9) reinforcement learning (9) graph neural network (9) attention mechanism (8) restricted boltzmann machine (7) variational autoencoder (7) deep neural network (6) out-of-distribution generalization (6) question answering (5) speech recognition (5)

Papers

Extendable Planning via Multiscale Diffusion AAAI 2026
Outsourced Diffusion Sampling: Efficient Posterior Inference in Latent Spaces of Generative Models ICML 2025
HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models ICLR 2025
Rejecting Hallucinated State Targets during Planning ICML 2025
Structure Language Models for Protein Conformation Generation ICLR 2025
Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning ICLR 2025
Monte Carlo Tree Diffusion for System 2 Planning ICML 2025
VCR: A Task for Pixel-Level Complex Reasoning in Vision Language Models via Restoring Occluded Text ICLR 2025
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N ICML 2025
Geometric Signatures of Compositionality Across a Language Model’s Lifetime ACL 2025
On the Transfer of Object-Centric Representation Learning ICLR 2025
RL, but don’t do anything I wouldn’t do UAI 2025
Can a Bayesian Oracle Prevent Harm from an Agent? UAI 2025
Action abstractions for amortized sampling ICLR 2025
BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks ICLR 2025
MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation ICLR 2025
Towards Improving Exploration through Sibling Augmented GFlowNets ICLR 2025
Adaptive teachers for amortized samplers ICLR 2025
Ant Colony Sampling with GFlowNets for Combinatorial Optimization AISTATS 2025
AssembleFlow: Rigid Flow Matching with Inertial Frames for Molecular Assembly ICLR 2025
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets ICLR 2025
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold ICLR 2025
Towards a Formal Theory of Representational Compositionality ICML 2025
Improving Gradient-Guided Nested Sampling for Posterior Inference ICML 2024
Amortizing intractable inference in diffusion models for vision, language, and control NIPS 2024
Pre-Training and Fine-Tuning Generative Flow Networks ICLR 2024
Tree Cross Attention ICLR 2024
Delta-AI: Local objectives for amortized inference in sparse graphical models ICLR 2024
Amortizing intractable inference in large language models ICLR 2024
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning ICLR 2024
Local Search GFlowNets ICLR 2024
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities ICML 2024
Regeneration Learning: A Learning Paradigm for Data Generation AAAI 2024
Simulation-Free SchrΓΆdinger Bridges via Score and Flow Matching AISTATS 2024
Cycle Consistency Driven Object Discovery ICLR 2024
Object centric architectures enable efficient causal representation learning ICLR 2024
PhyloGFN: Phylogenetic inference with generative flow networks ICLR 2024
Memory Efficient Neural Processes via Constant Memory Attention Block ICML 2024
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving NIPS 2024
RGFN: Synthesizable Molecular Generation Using GFlowNets NIPS 2024
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization ICLR 2024
Improved off-policy training of diffusion samplers NIPS 2024
Trajectory Flow Matching with Applications to Clinical Time Series Modelling NIPS 2024
Expected flow networks in stochastic environments and two-player zero-sum games ICLR 2024
Discrete Probabilistic Inference as Control in Multi-path Environments UAI 2024
PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design JMLR 2024
Learning to Scale Logits for Temperature-Conditional GFlowNets ICML 2024
SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data NIPS 2023
The Effect of Diversity in Meta-Learning AAAI 2023
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness AAAI 2023
Predictive Inference with Feature Conformal Prediction ICLR 2023
Latent State Marginalization as a Low-cost Approach for Improving Exploration ICLR 2023
Robust and Controllable Object-Centric Learning through Energy-based Models ICLR 2023
GFlowNets and variational inference ICLR 2023
Generative Augmented Flow Networks ICLR 2023
Equivariance with Learned Canonicalization Functions ICML 2023
Multi-Objective GFlowNets ICML 2023
GFlowNet-EM for Learning Compositional Latent Variable Models ICML 2023
FAENet: Frame Averaging Equivariant GNN for Materials Modeling ICML 2023
Interventional Causal Representation Learning ICML 2023
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning ICML 2023
MixupE: Understanding and improving Mixup from directional derivative perspective UAI 2023
Stochastic Generative Flow Networks UAI 2023
A theory of continuous generative flow networks ICML 2023
GFlowOut: Dropout with Generative Flow Networks ICML 2023
Learning GFlowNets From Partial Episodes For Improved Convergence And Stability ICML 2023
Better Training of GFlowNets with Local Credit and Incomplete Trajectories ICML 2023
Hyena Hierarchy: Towards Larger Convolutional Language Models ICML 2023
Discrete Key-Value Bottleneck ICML 2023
Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning ICLR 2023
Latent Bottlenecked Attentive Neural Processes ICLR 2023
Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context NIPS 2023
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets NIPS 2023
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions NIPS 2023
Reusable Slotwise Mechanisms NIPS 2023
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL NIPS 2023
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network NIPS 2023
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution NIPS 2023
GEO-Bench: Toward Foundation Models for Earth Monitoring NIPS 2023
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets NIPS 2023
Combining Parameter-efficient Modules for Task-level Generalisation EACL 2023
GFlowNet Foundations JMLR 2023
Benchmarking Graph Neural Networks JMLR 2023
Neural Attentive Circuits NIPS 2022
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods ICLR 2022
Chunked Autoregressive GAN for Conditional Waveform Synthesis ICLR 2022
Graph Neural Networks with Learnable Structural and Positional Representations ICLR 2022
Coordination Among Neural Modules Through a Shared Global Workspace ICLR 2022
Compositional Attention: Disentangling Search and Retrieval ICLR 2022
Properties from mechanisms: an equivariance perspective on identifiable representation learning ICLR 2022
Unifying Likelihood-free Inference with Black-box Optimization and Beyond ICLR 2022
Continuous-Time Meta-Learning with Forward Mode Differentiation ICLR 2022
Building Robust Ensembles via Margin Boosting ICML 2022
Generative Flow Networks for Discrete Probabilistic Modeling ICML 2022
Temporal abstractions-augmented temporally contrastive learning: An alternative to the Laplacian in RL UAI 2022
Bayesian structure learning with generative flow networks UAI 2022
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints NIPS 2022
Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning NIPS 2022
Trajectory balance: Improved credit assignment in GFlowNets NIPS 2022
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning NIPS 2022
Weakly Supervised Representation Learning with Sparse Perturbations NIPS 2022
Is a Modular Architecture Enough? NIPS 2022
MAgNet: Mesh Agnostic Neural PDE Solver NIPS 2022
Multi-scale Feature Learning Dynamics: Insights for Double Descent ICML 2022
Biological Sequence Design with GFlowNets ICML 2022
Towards Scaling Difference Target Propagation by Learning Backprop Targets ICML 2022
VIM: Variational Independent Modules for Video Prediction CLEAR 2022
Systematic generalisation with group invariant predictions ICLR 2021
Object-Centric Image Generation from Layouts AAAI 2021
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning ICLR 2021
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation NIPS 2021
FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters ICCV 2021
Neural Production Systems NIPS 2021
Dynamic Inference with Neural Interpreters NIPS 2021
The Causal-Neural Connection: Expressiveness, Learnability, and Inference NIPS 2021
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization NIPS 2021
Discrete-Valued Neural Communication NIPS 2021
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning NIPS 2021
Gradient Starvation: A Learning Proclivity in Neural Networks NIPS 2021
Recurrent Independent Mechanisms ICLR 2021
Predicting Infectiousness for Proactive Contact Tracing ICLR 2021
Saliency is a Possible Red Herring When Diagnosing Poor Generalization ICLR 2021
Learning Neural Generative Dynamics for Molecular Conformation Generation ICLR 2021
Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments ICLR 2021
Fast And Slow Learning Of Recurrent Independent Mechanisms ICLR 2021
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs ICLR 2021
Spatially Structured Recurrent Modules ICLR 2021
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming ICML 2021
An Analysis of the Adaptation Speed of Causal Models AISTATS 2021
Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers AISTATS 2021
GraphMix: Improved Training of GNNs for Semi-Supervised Learning AAAI 2021
Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting AAAI 2021
Visual Concept Reasoning Networks AAAI 2021
hBERT + BiasCorp - Fighting Racism on the Web EACL 2021
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models AAAI 2021
Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies AAAI 2021
Revisiting Fundamentals of Experience Replay ICML 2020
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning ICML 2020
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules ICML 2020
Small-GAN: Speeding up GAN Training using Core-Sets ICML 2020
Perceptual Generative Autoencoders ICML 2020
Experience Grounds Language EMNLP 2020
On the interplay between noise and curvature and its effect on optimization and generalization AISTATS 2020
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives ICLR 2020
Untangling tradeoffs between recurrence and self-attention in artificial neural networks NIPS 2020
Hybrid Models for Learning to Branch NIPS 2020
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling NIPS 2020
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting ICLR 2020
Learning the Arrow of Time for Problems in Reinforcement Learning ICLR 2020
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach ACL 2020
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget ICLR 2020
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning ECCV 2020
Compositional Generalization by Factorizing Alignment and Translation ACL 2020
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms ICLR 2020
Combating False Negatives in Adversarial Imitation Learning (Student Abstract) AAAI 2020
Interpolation Consistency Training for Semi-supervised Learning IJCAI 2019
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input NIPS 2019
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis NIPS 2019
Unsupervised State Representation Learning in Atari NIPS 2019
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics NIPS 2019
Variational Temporal Abstraction NIPS 2019
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets NIPS 2019
Gradient based sample selection for online continual learning NIPS 2019
On Adversarial Mixup Resynthesis NIPS 2019
Wasserstein Dependency Measure for Representation Learning NIPS 2019
Towards Non-Saturating Recurrent Units for Modelling Long-Term Dependencies AAAI 2019
Combined Reinforcement Learning via Abstract Representations AAAI 2019
Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study ACL 2019
Interactive Language Learning by Question Answering EMNLP 2019
Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization ICCV 2019
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction ICCV 2019
Modeling the Long Term Future in Model-Based Reinforcement Learning ICLR 2019
An Empirical Study of Example Forgetting during Deep Neural Network Learning ICLR 2019
Recall Traces: Backtracking Models for Efficient Reinforcement Learning ICLR 2019
Probabilistic Planning with Sequential Monte Carlo methods ICLR 2019
InfoBot: Transfer and Exploration via the Information Bottleneck ICLR 2019
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning ICLR 2019
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length ICLR 2019
Learning deep representations by mutual information estimation and maximization ICLR 2019
h-detach: Modifying the LSTM Gradient Towards Better Optimization ICLR 2019
Deep Graph Infomax ICLR 2019
Quaternion Recurrent Neural Networks ICLR 2019
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces ICLR 2019
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations ICML 2019
GMNN: Graph Markov Neural Networks ICML 2019
On the Spectral Bias of Neural Networks ICML 2019
Manifold Mixup: Better Representations by Interpolating Hidden States ICML 2019
Interactive Language Learning by Question Answering IJCNLP 2019
Learning Problem-Agnostic Speech Representations from Multiple Self-Supervised Tasks INTERSPEECH 2019
Speech Model Pre-Training for End-to-End Spoken Language Understanding INTERSPEECH 2019
Learning Speaker Representations with Mutual Information INTERSPEECH 2019
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance ACL 2018
Deep Complex Networks ICLR 2018
Residual Connections Encourage Iterative Inference ICLR 2018
Twin Networks: Matching the Future for Sequence Generation ICLR 2018
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning ICLR 2018
Graph Attention Networks ICLR 2018
Fraternal Dropout ICLR 2018
Boundary Seeking GANs ICLR 2018
Commonsense mining as knowledge base completion? A study on the impact of novelty NAACL 2018
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences ACL 2018
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations JMLR 2018
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering EMNLP 2018
Mutual Information Neural Estimation ICML 2018
Focused Hierarchical RNNs for Conditional Sequence Processing ICML 2018
Twin Regularization for Online Speech Recognition INTERSPEECH 2018
Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition INTERSPEECH 2018
Neural Models for Key Phrase Extraction and Question Generation ACL 2018
Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding NIPS 2018
Bayesian Model-Agnostic Meta-Learning NIPS 2018
Image-to-image translation for cross-domain disentanglement NIPS 2018
MetaGAN: An Adversarial Approach to Few-Shot Learning NIPS 2018
Dendritic cortical microcircuits approximate the backpropagation algorithm NIPS 2018
Improving Speech Recognition by Revising Gated Recurrent Units INTERSPEECH 2017
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net NIPS 2017
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space CVPR 2017
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models NIPS 2017
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses ACL 2017
A Closer Look at Memorization in Deep Networks ICML 2017
Sharp Minima Can Generalize For Deep Nets ICML 2017
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models NIPS 2017
Z-Forcing: Training Stochastic Recurrent Networks NIPS 2017
Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition INTERSPEECH 2017
Unitary Evolution Recurrent Neural Networks ICML 2016
Bidirectional Helmholtz Machines ICML 2016
Noisy Activation Functions ICML 2016
Architectural Complexity Measures of Recurrent Neural Networks NIPS 2016
Professor Forcing: A New Algorithm for Training Recurrent Networks NIPS 2016
A Character-level Decoder without Explicit Segmentation for Neural Machine Translation ACL 2016
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus ACL 2016
Pointing the Unknown Words ACL 2016
Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks INTERSPEECH 2016
Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism NAACL 2016
On Multiplicative Integration with Recurrent Neural Networks NIPS 2016
Knowledge Matters: Importance of Prior Information for Optimization JMLR 2016
Binarized Neural Networks NIPS 2016
Deconstructing the Ladder Network Architecture ICML 2016
Gated Feedback Recurrent Neural Networks ICML 2015
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments ICML 2015
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention ICML 2015
On Using Very Large Target Vocabulary for Neural Machine Translation ACL 2015
Equilibrated adaptive learning rates for non-convex optimization NIPS 2015
On Using Very Large Target Vocabulary for Neural Machine Translation IJCNLP 2015
Attention-Based Models for Speech Recognition NIPS 2015
BinaryConnect: Training Deep Neural Networks with binary weights during propagations NIPS 2015
A Recurrent Latent Variable Model for Sequential Data NIPS 2015
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization NIPS 2014
Deep Generative Stochastic Networks Trainable by Backprop ICML 2014
Marginalized Denoising Auto-encoders for Nonlinear Representations ICML 2014
On the Number of Linear Regions of Deep Neural Networks NIPS 2014
Generative Adversarial Nets NIPS 2014
Iterative Neural Autoregressive Distribution Estimator NADE-k NIPS 2014
How transferable are features in deep neural networks? NIPS 2014
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation EMNLP 2014
What Regularized Auto-Encoders Learn from the Data-Generating Distribution JMLR 2014
Maxout Networks ICML 2013
Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions AISTATS 2013
Better Mixing via Deep Representations ICML 2013
Multi-Prediction Deep Boltzmann Machines NIPS 2013
Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs NIPS 2013
Generalized Denoising Auto-Encoders as Generative Models NIPS 2013
On the difficulty of training recurrent neural networks ICML 2013
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing AISTATS 2012
Deep Learning for NLP (without Magic) ACL 2012
Random Search for Hyper-Parameter Optimization JMLR 2012
Learning Algorithms for the Classification Restricted Boltzmann Machine JMLR 2012
Deep Sparse Rectifier Neural Networks AISTATS 2011
Discussion of β€œThe Neural Autoregressive Distribution Estimator” AISTATS 2011
Shallow vs. Deep Sum-Product Networks NIPS 2011
Algorithms for Hyper-Parameter Optimization NIPS 2011
The Manifold Tangent Classifier NIPS 2011
On Tracking The Partition Function NIPS 2011
A Spike and Slab Restricted Boltzmann Machine AISTATS 2011
Deep Learners Benefit More from Out-of-Distribution Examples AISTATS 2011
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines AISTATS 2010
Why Does Unsupervised Pre-training Help Deep Learning? JMLR 2010
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion JMLR 2010
Understanding the difficulty of training deep feedforward neural networks AISTATS 2010
Word Representations: A Simple and General Method for Semi-Supervised Learning ACL 2010
Why Does Unsupervised Pre-training Help Deep Learning? AISTATS 2010
Slow, Decorrelated Features for Pretraining Complex Cell-like Networks NIPS 2009
Exploring Strategies for Training Deep Neural Networks JMLR 2009
Quadratic Features and Deep Architectures for Chunking NAACL 2009
Incorporating Functional Knowledge in Neural Networks JMLR 2009
An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism NIPS 2009
Augmented Functional Time Series Representation and Forecasting with Gaussian Processes NIPS 2007
Topmoumoute Online Natural Gradient Algorithm NIPS 2007
Learning the 2-D Topology of Images NIPS 2007
Greedy Layer-Wise Training of Deep Networks NIPS 2006
No Unbiased Estimator of the Variance of K-Fold Cross-Validation JMLR 2004
Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models ACL 2004
Extensions to Metric-Based Model Selection JMLR 2003
A Neural Probabilistic Language Model JMLR 2003