Svetha Venkatesh

Artificial Intelligence · 1996–2020

A researcher in the Science corpus: 22 papers; trusts 777 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 286 citations into it; #7,140 of 35,345 by trust here, #3,783 by citations
Computer Vision and Pattern Recognition — 189 citations into it; #5,496 of 21,575 by trust here, #4,726 by citations
Model Architectures — 61 citations into it
Optimization & Training — 55 citations into it
Management Science and Operations Research — 42 citations into it
Computational Theory and Mathematics — 38 citations into it; #9,841 of 12,182 by trust here, #9,804 by citations
Question Answering & Reading Comprehension — 38 citations into it; #174 of 335 by trust here, #150 by citations
Natural Language Processing — 35 citations into it

Whom Svetha Venkatesh trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

Who trusts Svetha Venkatesh overall

Papers from Svetha Venkatesh's perspective

Ordered by how strongly the researchers Svetha Venkatesh trusts endorse each paper. Same corpus, a different order for every researcher.

Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
ImageNet classification with deep convolutional neural networks
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Long Short-Term Memory
Attention is All you Need (Neural Information Processing Systems)
Gradient-based learning applied to document recognition
ImageNet: A large-scale hierarchical image database
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Learning Multiple Layers of Features from Tiny Images
Going deeper with convolutions

Their papers

Video abstraction: A systematic review and classification (2007) — 937 citations
Hierarchical Conditional Relation Networks for Video Question Answering (2020) — 301 citations
Activity recognition and abnormality detection with the switching hidden semi-Markov model (2005) — 613 citations
On catastrophic forgetting and mode collapse in Generative Adversarial Networks (2018) — 64 citations
Column Networks for Collective Classification (2016) — 152 citations
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model (2005) — 374 citations
Hierarchical Conditional Relation Networks for Multimodal Video Question Answering (2020) — 30 citations
Nonnegative shared subspace learning and its application to social media retrieval (2010) — 105 citations
Stable Bayesian optimization (2018) — 33 citations
Graph Classification via Deep Learning with Virtual Nodes (2017) — 57 citations
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs (2019) — 60 citations
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View (2016) — 874 citations
Bayesian Optimization for Adaptive Experimental Design: A Review (2020) — 398 citations
Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety (2019) — 316 citations
Neural Reasoning, Fast and Slow, for Video Question Answering (2019) — 14 citations
Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) — 1,733 citations
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos (2019) — 321 citations
Predicting healthcare trajectories from medical records: A deep learning approach (2017) — 393 citations
$\mathtt {Deepr}$: A Convolutional Net for Medical Records (2016) — 400 citations
DeepCare: A Deep Dynamic Memory Model for Predictive Medicine (2016) — 304 citations