Hannah Francis Larochelle
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Hannah Francis Larochellecomputer-science Degrees
Computer Science
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Algorithms
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Machine Learning
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Computer Science
Hannah Francis Larochelle's Degrees
- PhD Computer Science Stanford University
- Masters Computer Science Stanford University
- Bachelors Computer Science Stanford University
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Why Is Hannah Francis Larochelle Influential?
(Suggest an Edit or Addition)Hannah Francis Larochelle's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion (2010) (6316)
- Extracting and composing robust features with denoising autoencoders (2008) (6262)
- Practical Bayesian Optimization of Machine Learning Algorithms (2012) (5994)
- Domain-Adversarial Training of Neural Networks (2015) (5733)
- Greedy Layer-Wise Training of Deep Networks (2006) (4138)
- Optimization as a Model for Few-Shot Learning (2016) (2660)
- Brain tumor segmentation with Deep Neural Networks (2015) (2344)
- Autoencoding beyond pixels using a learned similarity metric (2015) (1711)
- Exploring Strategies for Training Deep Neural Networks (2009) (1115)
- An empirical evaluation of deep architectures on problems with many factors of variation (2007) (1078)
- Describing Videos by Exploiting Temporal Structure (2015) (972)
- Meta-Learning for Semi-Supervised Few-Shot Classification (2018) (917)
- Classification using discriminative restricted Boltzmann machines (2008) (849)
- MADE: Masked Autoencoder for Distribution Estimation (2015) (626)
- The Neural Autoregressive Distribution Estimator (2011) (510)
- Machine behaviour (2019) (495)
- Zero-data Learning of New Tasks (2008) (420)
- Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples (2019) (418)
- Learning to combine foveal glimpses with a third-order Boltzmann machine (2010) (409)
- Modulating early visual processing by language (2017) (397)
- Efficient Learning of Deep Boltzmann Machines (2010) (384)
- Advances in Neural Information Processing Systems 31 (2018) (354)
- GuessWhat?! Visual Object Discovery through Multi-modal Dialogue (2016) (349)
- An Autoencoder Approach to Learning Bilingual Word Representations (2014) (335)
- Learning Algorithms for the Classification Restricted Boltzmann Machine (2012) (297)
- Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations (2016) (293)
- Neural Autoregressive Distribution Estimation (2016) (246)
- A Neural Autoregressive Topic Model (2012) (216)
- Movie Description (2016) (216)
- RNADE: The real-valued neural autoregressive density-estimator (2013) (206)
- The Hanabi Challenge: A New Frontier for AI Research (2019) (203)
- Learning Where to Attend with Deep Architectures for Image Tracking (2011) (197)
- Domain-Adversarial Neural Networks (2014) (191)
- Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research (2015) (185)
- Language GANs Falling Short (2018) (164)
- Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program) (2020) (162)
- A Meta-Learning Perspective on Cold-Start Recommendations for Items (2017) (153)
- A Deep and Tractable Density Estimator (2013) (145)
- InfoBot: Transfer and Exploration via the Information Bottleneck (2019) (132)
- Correlational Neural Networks (2015) (127)
- Conditional Restricted Boltzmann Machines for Structured Output Prediction (2011) (122)
- Learning Neural Causal Models from Unknown Interventions (2019) (122)
- Recurrent Mixture Density Network for Spatiotemporal Visual Attention (2016) (121)
- Revisiting Fundamentals of Experience Replay (2020) (109)
- HoME: a Household Multimodal Environment (2017) (98)
- Training Restricted Boltzmann Machines on Word Observations (2012) (94)
- A Convolutional Neural Network Approach to Brain Tumor Segmentation (2015) (82)
- Deep Learning Trends for Focal Brain Pathology Segmentation in MRI (2016) (77)
- Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling (2020) (75)
- Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality (2015) (75)
- Dynamic Capacity Networks (2015) (74)
- A Universal Representation Transformer Layer for Few-Shot Image Classification (2020) (73)
- Hyperbolic Discounting and Learning over Multiple Horizons (2019) (73)
- Hierarchical Memory Networks (2016) (67)
- Efficient Interactive Brain Tumor Segmentation as Within-Brain kNN Classification (2014) (65)
- An Infinite Restricted Boltzmann Machine (2015) (65)
- Topic Modeling of Multimodal Data: An Autoregressive Approach (2014) (63)
- Non-Local Manifold Parzen Windows (2005) (61)
- Nonparametric guidance of autoencoder representations using label information (2012) (61)
- A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data (2014) (59)
- Recall Traces: Backtracking Models for Efficient Reinforcement Learning (2018) (56)
- Learning attentional policies for tracking and recognition in video with deep networks (2011) (56)
- Within-brain classification for brain tumor segmentation (2015) (54)
- Disentangling the independently controllable factors of variation by interacting with the world (2018) (53)
- Video Description Generation Incorporating Spatio-Temporal Features and a Soft-Attention Mechanism (2015) (47)
- Blindfold Baselines for Embodied QA (2018) (43)
- Nonlocal Estimation of Manifold Structure (2006) (43)
- Learn to Track: Deep Learning for Tractography (2017) (40)
- Learning Multilingual Word Representations using a Bag-of-Words Autoencoder (2014) (40)
- A RAD approach to deep mixture models (2019) (39)
- Curriculum By Smoothing (2020) (39)
- Learning a Universal Template for Few-shot Dataset Generalization (2021) (37)
- Small-GAN: Speeding Up GAN Training Using Core-sets (2019) (36)
- Meta-Learning for Batch Mode Active Learning (2018) (35)
- Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition (2015) (35)
- Guest Editors' Introduction: Special Section on Learning Deep Architectures (2013) (32)
- A Neural Autoregressive Approach to Attention-based Recognition (2015) (32)
- Agnostic Bayesian Learning of Ensembles (2014) (32)
- Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks (2020) (29)
- Deep Learning using Robust Interdependent Codes (2009) (29)
- Document Neural Autoregressive Distribution Estimation (2016) (28)
- Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning (2022) (27)
- PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes (2014) (26)
- Classification of Sets using Restricted Boltzmann Machines (2011) (26)
- Tractable Multivariate Binary Density Estimation and the Restricted Boltzmann Forest (2010) (26)
- An Effective Anti-Aliasing Approach for Residual Networks (2020) (25)
- Sequential Model-Based Ensemble Optimization (2014) (25)
- Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction (2019) (25)
- DIBS: Diversity inducing Information Bottleneck in Model Ensembles (2020) (24)
- Learning to rank by aggregating expert preferences (2012) (21)
- Interpretable Multi-Modal Hate Speech Detection (2021) (19)
- Impact of Aliasing on Generalization in Deep Convolutional Networks (2021) (19)
- Few-Shot Learning (2020) (18)
- Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification (2019) (18)
- On Nonparametric Guidance for Learning Autoencoder Representations (2011) (16)
- Traffic Analytics With Low-Frame-Rate Videos (2018) (16)
- Autotagging music with conditional restricted Boltzmann machines (2011) (16)
- Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark (2021) (15)
- Brain Tumor Segmentation by a Generative Model with a Prior on Tumor Shape (2015) (15)
- Learning Graph Structure With A Finite-State Automaton Layer (2020) (15)
- ICLR Reproducibility Challenge 2019 (2019) (14)
- Teaching Algorithmic Reasoning via In-context Learning (2022) (13)
- Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-Time (2019) (13)
- Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge (2020) (13)
- DETONATION CLASSIFICATION FROM ACOUSTIC SIGNATURE WITH THE RESTRICTED BOLTZMANN MACHINE (2012) (13)
- Fortuitous Forgetting in Connectionist Networks (2022) (12)
- Loss-sensitive Training of Probabilistic Conditional Random Fields (2011) (12)
- On Catastrophic Interference in Atari 2600 Games (2020) (12)
- Matching Feature Sets for Few-Shot Image Classification (2022) (10)
- Leveraging user libraries to bootstrap collaborative filtering (2014) (9)
- Automatic Machine Learning (AutoML) (2015) (9)
- Repository-Level Prompt Generation for Large Language Models of Code (2022) (8)
- Diversity inducing Information Bottleneck in Model Ensembles (2020) (8)
- Multiscale sequence modeling with a learned dictionary (2017) (8)
- NeurIPS 2019 Reproducibility Challenge (2020) (8)
- Opportunity Cost in Bayesian Optimization (2011) (7)
- A Convolutional Neural Network Approach to Brain Lesion Segmentation (2015) (7)
- Curriculum By Texture (2020) (5)
- Learning to Combine Per-Example Solutions for Neural Program Synthesis (2021) (5)
- A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches (2021) (5)
- Guest Editorial: Deep Learning for Multimedia Computing (2015) (4)
- Few-shot Learning with Meta-Learning: Progress Made and Challenges Ahead (2018) (4)
- Uniform Priors for Data-Efficient Transfer. (2020) (3)
- Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015 (2015) (3)
- On-the-Fly Adaptation of Source Code Models using Meta-Learning (2020) (3)
- Distributed Representation Prediction for Generalization to New Words (2006) (2)
- Uniform Priors for Data-Efficient Learning (2022) (2)
- Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality, CVSC@EACL 2014, Gothenburg, Sweden, April 26-30, 2014 (2014) (2)
- Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions (2022) (2)
- UvA-DARE (Digital Academic Repository) SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks (2020) (1)
- PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes (2014) (1)
- Are Few-shot Learning Benchmarks Too Simple ? (2019) (1)
- InfoBot: Structured Exploration in ReinforcementLearning Using Information Bottleneck (2019) (1)
- Etude de techniques d'apprentissage non-supervise pour l'amelioration de l'entrainement supervise de modeles connexionnistes (2009) (1)
- Semiparametric Latent Variable Models for Guided Representation (2011) (1)
- A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation (2013) (1)
- Comparing different statistical approaches to determine carcass and cut composition. (2014) (0)
- Deep Scale-spaces: Equivariance Over Scale (2021) (0)
- Learning Ordinary Differential Equations with the Line Integral Loss Function (2022) (0)
- Green High Five: Vertical Gardens along the Egnatia Corridor (2019) (0)
- M ETA-L EARNING FOR S EMI-S UPERVISED F EW-S HOT (2018) (0)
- Learned Equivariant Rendering without Transformation Supervision (2020) (0)
- Self-Supervised Equivariant Scene Synthesis from Video (2021) (0)
- Bayesian Alignments of Warped Multi-Output Gaussian Processes (2018) (0)
- Category: Learning Algorithms Deep Woods (2008) (0)
- UvA-DARE (Digital Academic Deep Scale-spaces: Equivariance Over Scale (2021) (0)
- Machine behaviour (2019) (0)
- Uniform Priors for Meta-Learning (2010) (0)
- Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference (2019) (0)
- UvA-DARE (Digital Academic Repository) Combinatorial Bayesian Optimization using the Graph Cartesian Product (2021) (0)
- Generalization to a zero-data task: an empirical study (0)
- On the Abilities of Mathematical Extrapolation with Implicit Models (2022) (0)
- M ETA -D ATASET : A D ATASET OF D ATASETS FOR L EARNING TO L EARN FROM F EW E XAMPLES (2020) (0)
- Titre : Title : Movie description Auteurs : (2018) (0)
- UvA-DARE (Digital Academic Repository) Deep Scale-spaces: Equivariance Over Scale (2021) (0)
- O ct 2 01 9 S MALL-GAN : S PEEDING UP GAN T RAINING USING C ORES ETS (2019) (0)
- UvA-DARE (Digital Academic Repository) Integer Discrete Flows and Lossless Compression Integer Discrete Flows and Lossless Compression (2019) (0)
- Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action Corrections (2022) (0)
- The Neural Autoregressive Distribution Estimator (2011) (0)
- A Neural Autoregressive Approach to Attention-based Recognition (2014) (0)
- Consider a Programmer Implementing a Scheduler for Their Distributed System. Although the Overall (0)
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What Schools Are Affiliated With Hannah Francis Larochelle?
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