Richard Zemel
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Computer Science
Why Is Richard Zemel Influential?
(Suggest an Edit or Addition)According to Wikipedia, Richard Stanley Zemel is a Canadian-American computer scientist and professor at Columbia University, Department of Computer Science, and a leading figure in the field of Machine Learning and Computer Vision.
Richard Zemel'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
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention (2015) (8487)
- Prototypical Networks for Few-shot Learning (2017) (4882)
- Fairness through awareness (2011) (2521)
- Gated Graph Sequence Neural Networks (2015) (2502)
- Skip-Thought Vectors (2015) (2100)
- Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books (2015) (1817)
- Learning Fair Representations (2013) (1342)
- The Helmholtz Machine (1995) (1247)
- Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models (2014) (1186)
- Autoencoders, Minimum Description Length and Helmholtz Free Energy (1993) (1129)
- Understanding the Effective Receptive Field in Deep Convolutional Neural Networks (2016) (1096)
- Multiscale conditional random fields for image labeling (2004) (1046)
- Meta-Learning for Semi-Supervised Few-Shot Classification (2018) (917)
- Shortcut learning in deep neural networks (2020) (881)
- Generative Moment Matching Networks (2015) (739)
- Information processing with population codes (2000) (738)
- Multimodal Neural Language Models (2014) (626)
- Exploring Models and Data for Image Question Answering (2015) (597)
- Neural Relational Inference for Interacting Systems (2018) (587)
- Learning Adversarially Fair and Transferable Representations (2018) (484)
- Causal Effect Inference with Deep Latent-Variable Models (2017) (481)
- Inference and computation with population codes. (2003) (454)
- Probabilistic Interpretation of Population Codes (1996) (445)
- THE VARIATIONAL FAIR AUTOENCODER (2016) (307)
- Collaborative prediction and ranking with non-random missing data (2009) (286)
- Collaborative Filtering and the Missing at Random Assumption (2007) (282)
- End-to-End Instance Segmentation with Recurrent Attention (2016) (268)
- Object-based attention and occlusion: evidence from normal participants and a computational model. (1998) (225)
- Learning and Incorporating Top-Down Cues in Image Segmentation (2006) (224)
- Flexibly Fair Representation Learning by Disentanglement (2019) (212)
- Few-Shot Learning Through an Information Retrieval Lens (2017) (207)
- Input Warping for Bayesian Optimization of Non-Stationary Functions (2014) (202)
- Efficient Graph Generation with Graph Recurrent Attention Networks (2019) (196)
- The Variational Fair Autoencoder (2015) (195)
- LanczosNet: Multi-Scale Deep Graph Convolutional Networks (2019) (168)
- Environment Inference for Invariant Learning (2020) (152)
- Incremental Few-Shot Learning with Attention Attractor Networks (2018) (149)
- HOP-MAP: Efficient Message Passing with High Order Potentials (2010) (148)
- A Divergence Minimization Perspective on Imitation Learning Methods (2019) (148)
- Proximity Graphs for Clustering and Manifold Learning (2004) (142)
- Learning to generate images with perceptual similarity metrics (2015) (141)
- Image Question Answering: A Visual Semantic Embedding Model and a New Dataset (2015) (140)
- Excessive Invariance Causes Adversarial Vulnerability (2018) (138)
- A minimum description length framework for unsupervised learning (1994) (127)
- Understanding the Origins of Bias in Word Embeddings (2018) (125)
- The Toronto Paper Matching System: An automated paper-reviewer assignment system (2013) (122)
- Deep Spectral Clustering Learning (2017) (118)
- Experience-Dependent Perceptual Grouping and Object-Based Attention (2002) (114)
- BoltzRank: learning to maximize expected ranking gain (2009) (113)
- Active Collaborative Filtering (2002) (107)
- A Model for Encoding Multiple Object Motions and Self-Motion in Area MST of Primate Visual Cortex (1998) (105)
- Learning Latent Subspaces in Variational Autoencoders (2018) (103)
- Ranking via Sinkhorn Propagation (2011) (102)
- Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data (2019) (100)
- Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer (2017) (97)
- Competition and Multiple Cause Models (1995) (97)
- A Gradient-Based Boosting Algorithm for Regression Problems (2000) (97)
- Learning to Segment Images Using Dynamic Feature Binding (1991) (93)
- Learning Deep Parsimonious Representations (2016) (88)
- Lending direction to neural networks (1995) (85)
- Training Deep Neural Networks via Direct Loss Minimization (2015) (84)
- The multiple multiplicative factor model for collaborative filtering (2004) (84)
- Generative versus discriminative training of RBMs for classification of fMRI images (2008) (83)
- Inference in Probabilistic Graphical Models by Graph Neural Networks (2018) (80)
- Reviving and Improving Recurrent Back-Propagation (2018) (79)
- Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes (2016) (78)
- A flexible generative model for preference aggregation (2012) (72)
- Learning Articulated Structure and Motion (2010) (70)
- Fast Population Coding (2007) (69)
- A Multiplicative Model for Learning Distributed Text-Based Attribute Representations (2014) (67)
- Automated Detection of Unusual Events on Stairs (2006) (66)
- Graph Partition Neural Networks for Semi-Supervised Classification (2018) (65)
- On the Representational Efficiency of Restricted Boltzmann Machines (2013) (64)
- Classifying NBA Offensive Plays Using Neural Networks (2016) (63)
- End-to-End Instance Segmentation and Counting with Recurrent Attention (2016) (61)
- Fast Exact Inference for Recursive Cardinality Models (2012) (59)
- A Framework for Optimizing Paper Matching (2011) (58)
- Aggregated Momentum: Stability Through Passive Damping (2018) (57)
- Learning Parts-Based Representations of Data (2006) (57)
- Structured Output Learning with High Order Loss Functions (2012) (57)
- Randomized Optimum Models for Structured Prediction (2012) (54)
- Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data (2020) (54)
- Lorentzian Distance Learning for Hyperbolic Representations (2019) (53)
- Recommender Systems, Missing Data and Statistical Model Estimation (2011) (51)
- Collaborative Ranking With 17 Parameters (2012) (51)
- Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling (2020) (51)
- Self Supervised Boosting (2002) (50)
- Learning Hybrid Models for Image Annotation with Partially Labeled Data (2008) (49)
- Learning Population Codes by Minimizing Description Length (1993) (47)
- Comparing Classification Methods for Longitudinal fMRI Studies (2010) (46)
- Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models (2019) (46)
- Adversarial Distillation of Bayesian Neural Network Posteriors (2018) (44)
- Cardinality Restricted Boltzmann Machines (2012) (43)
- Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes (2020) (42)
- Statistical models and sensory attention (1999) (42)
- Causal Modeling for Fairness in Dynamical Systems (2019) (41)
- Discovering Viewpoint-Invariant Relationships That Characterize Objects (1990) (39)
- Probabilistic Computation in Spiking Populations (2004) (38)
- The Elephant in the Room (2018) (38)
- Exploring Compositional High Order Pattern Potentials for Structured Output Learning (2013) (38)
- Learning a Universal Template for Few-shot Dataset Generalization (2021) (37)
- New learning methods for supervised and unsupervised preference aggregation (2014) (37)
- Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach (2020) (36)
- Developing Population Codes by Minimizing Description Length (1993) (34)
- Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning (2013) (33)
- A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks (2020) (33)
- Distributional Population Codes and Multiple Motion Models (1998) (33)
- A Neural Autoregressive Approach to Attention-based Recognition (2015) (32)
- Learning unbiased features (2014) (32)
- A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data (2013) (31)
- Localist Attractor Networks (2001) (31)
- Multiple Cause Vector Quantization (2002) (30)
- Understanding the Limitations of Conditional Generative Models (2019) (30)
- THE VARIATIONAL FAIR AUTO ENCODER (2015) (29)
- Encoding and Decoding Spikes for Dynamic Stimuli (2008) (28)
- Topological map learning from outdoor image sequences (2006) (27)
- Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification (2020) (27)
- Mean-Field Networks (2014) (27)
- Unsupervised Learning of Skeletons from Motion (2008) (26)
- Unsupervised Learning with Non-Ignorable Missing Data (2005) (26)
- Learning Flexible Features for Conditional Random Fields (2008) (25)
- Neural Guided Constraint Logic Programming for Program Synthesis (2018) (25)
- Latent topic random fields: Learning using a taxonomy of labels (2008) (24)
- Learning stick-figure models using nonparametric Bayesian priors over trees (2008) (24)
- Flexible Priors for Exemplar-based Clustering (2008) (24)
- SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies (2019) (23)
- Cortical Belief Networks (2003) (23)
- TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations (1989) (23)
- Graph Cuts is a Max-Product Algorithm (2011) (21)
- Joint Embeddings of Scene Graphs and Images (2017) (21)
- Learning to rank by aggregating expert preferences (2012) (21)
- High Order Regularization for Semi-Supervised Learning of Structured Output Problems (2014) (21)
- Dualing GANs (2017) (20)
- Occlusion, symmetry, and object-based attention: reply to Saiki (2000). (2000) (20)
- Reliable disparity estimation through selective integration (1998) (20)
- Efficient Sampling for Bipartite Matching Problems (2012) (19)
- Exchanging Lessons Between Algorithmic Fairness and Domain Generalization (2020) (18)
- Towards Generalizable Sentence Embeddings (2016) (18)
- Conditional Generative Models are not Robust (2019) (17)
- Combining Probabilistic Population Codes (1997) (17)
- Wandering Within a World: Online Contextualized Few-Shot Learning (2020) (17)
- Combining discriminative features to infer complex trajectories (2006) (17)
- Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes (2021) (17)
- Variational Model Inversion Attacks (2022) (16)
- Directional-Unit Boltzmann Machines (1992) (16)
- Probabilistic n-Choose-k Models for Classification and Ranking (2012) (15)
- Characterizing response behavior in multisensory perception with conflicting cues (2008) (15)
- Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling (2020) (14)
- Predict Responsibly: Increasing Fairness by Learning To Defer (2017) (14)
- Deep Ensembles Work, But Are They Necessary? (2022) (13)
- Efficient Multiple Instance Metric Learning Using Weakly Supervised Data (2017) (13)
- Probabilistic sequential independent components analysis (2004) (13)
- CRF framework for supervised preference aggregation (2013) (13)
- Theoretical bounds on estimation error for meta-learning (2020) (13)
- Minimizing Description Length in an Unsupervised Neural Network (2000) (12)
- Proceedings of the 24th International Conference on Neural Information Processing Systems (2011) (12)
- SketchEmbedNet: Learning Novel Concepts by Imitating Drawings (2020) (12)
- Loss-sensitive Training of Probabilistic Conditional Random Fields (2011) (12)
- Gradient-based Optimization of Neural Network Architecture (2018) (11)
- Minimum description length analysis (1998) (11)
- Dimensionality Reduction for Representing the Knowledge of Probabilistic Models (2018) (10)
- Efficient Feature Learning Using Perturb-and-MAP (2013) (10)
- Encoding multiple orientations in a recurrent network (2000) (9)
- Leveraging user libraries to bootstrap collaborative filtering (2014) (9)
- A Computational Framework for Slang Generation (2021) (9)
- Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain (2011) (9)
- Active Learning for Matching Problems (2012) (9)
- On the Expressive Power of Restricted Boltzmann Machines (2013) (9)
- Direct Loss Minimization for Training Deep Neural Nets (2015) (8)
- Population Coding with Motion Energy Filters: The Impact of Correlations (2008) (8)
- Few-shot Out-of-Distribution Detection (2020) (7)
- Leveraging Constraint Logic Programming for Neural Guided Program Synthesis (2018) (7)
- Grouping Components of Three-Dimensional Moving Objects in Area MST of Visual Cortex (1994) (7)
- Flexible Few-Shot Learning with Contextual Similarity (2020) (6)
- Efficient Parametric Projection Pursuit Density Estimation (2002) (6)
- Out-of-distribution Detection in Few-shot Classification (2019) (5)
- Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning (2019) (5)
- Differentially Private Decoding in Large Language Models (2022) (5)
- Proceedings of the 23rd International Conference on Neural Information Processing Systems (2010) (5)
- Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic (2022) (5)
- DISPATCHER: An Intelligent Approach to Factory Control (1986) (5)
- Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data (2018) (4)
- Slang Generation as Categorization (2019) (4)
- Stochastic Segmentation Trees for Multiple Ground Truths (2017) (4)
- Learning Articulated Skeletons from Motion (2007) (4)
- Unsupervised learning of object models (1993) (4)
- On Monotonic Linear Interpolation of Neural Network Parameters (2021) (4)
- Interpreting Graph Cuts as a Max-Product Algorithm (2011) (4)
- An Active Approach to Collaborative Filtering (2003) (4)
- A Generative Model for Attractor Dynamics (1999) (3)
- UvA-DARE (Digital Academic Repository) Neural Relational Inference for Interacting Systems Neural Relational Inference for Interacting Systems (2018) (3)
- Managing Uncertainty in Cue Combination (1999) (3)
- CUSTOMIZABLE FACIAL GESTURE RECOGNITION FOR IMPROVED ASSISTIVE TECHNOLOGY (2019) (3)
- Assessing AI Fairness in Finance (2022) (2)
- Disentanglement and Generalization Under Correlation Shifts (2021) (2)
- Identifying and Benchmarking Natural Out-of-Context Prediction Problems (2021) (2)
- NP-DRAW: A Non-Parametric Structured Latent Variable Modelfor Image Generation (2021) (2)
- Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data (2021) (2)
- Dynamic Cue Combination in Distributional Population Code Networks (2010) (2)
- High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks (2019) (2)
- Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada (2009) (2)
- Online Queries for Collaborative Filtering (2010) (2)
- Guest Editors' Introduction: Special Section on Higher Order Graphical Models in Computer Vision (2015) (2)
- Lorentzian Distance Learning (2019) (1)
- A Probabilistic Network Model of Population Responses (2007) (1)
- Online Unsupervised Learning of Visual Representations and Categories (2021) (1)
- Developing Population Codes For Object Instantiation Parameters (2001) (1)
- Few-Shot Attribute Learning (2021) (1)
- Semantically Informed Slang Interpretation (2022) (1)
- Unintended cue learning: Lessons for deep learning from experimental psychology (2020) (1)
- Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions (2022) (1)
- Few-shot Learning for Free by Modelling Global Class Structure (2018) (1)
- 6 Population Codes (2006) (0)
- A Neural Autoregressive Approach to Attention-based Recognition (2014) (0)
- Competing RBM density models for classification of fMRI images (2008) (0)
- Towards Environment-Invariant Representation Learning for Robust Task Transfer (2022) (0)
- Privacy in the Time of Language Models (2023) (0)
- Optimal adaptation of neural codes : An account of repetition suppression (2002) (0)
- Implications of Model Indeterminacy for Explanations of Automated Decisions (2022) (0)
- Probing Few-Shot Generalization with Attributes (2020) (0)
- A simple population code for a fast-changing world (2005) (0)
- G RADIENT-BASED OPTIMIZATION OF NEURAL NETWORK ARCHITECTURE (2018) (0)
- EXTENDING NETWORK NORMALIZATION SCHEMES (2017) (0)
- M ETA-L EARNING FOR S EMI-S UPERVISED F EW-S HOT (2018) (0)
- Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models (2022) (0)
- LANCZOSNET: MULTI-SCALE DEEP GRAPH CONVO- (2018) (0)
- Large Margin Learning with High Order Loss Functions (2011) (0)
- Incorporating Fairness in Large Scale NLU Systems (2023) (0)
- Comparing model predictions of response bias and variance in cue combination (2008) (0)
- Moving targets Interpreting population responses in area MT (1998) (0)
- Population Codes for Natural Dynamic Stimuli (2006) (0)
- NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation (Supplementary material) (2021) (0)
- Achieving Robust Neural Representations : An Account Of Repetition Suppression (2004) (0)
- Optimizing Long-term Social Welfare in Recommender Systems (Supplemental) (2020) (0)
- EW-SHOT L EARNING (2016) (0)
- Population Coding in a fast-changing world (2005) (0)
- Developing Population Codes For (1993) (0)
- Causal Modeling for Fairness in Dynamical Systems (Supplemental) (2020) (0)
- Representational pursuit: Population codes for dynamic environments (2005) (0)
- Selective Integration: A Model for Disparity Estimation (1996) (0)
- Randomized Optimum Models for Structured Prediction Randomized Optimum Models for Structured Prediction — Appendix 7 More Example RandOM Constructions (2012) (0)
- Dynamic population codes for sensorimotor processing (2006) (0)
- Learning to generate images and their descriptions (keynote) (2016) (0)
- SURFSUP: Learning Fluid Simulation for Novel Surfaces (2023) (0)
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What Schools Are Affiliated With Richard Zemel?
Richard Zemel is affiliated with the following schools:
