Mitesh M. Khapra

Artificial Intelligence · 2014–2024

A researcher in the Science corpus: 20 papers; trusts 1,299 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 566 citations into it; #5,740 of 35,345 by trust here, #3,816 by citations
• Natural Language Processing — 486 citations into it; #1,790 of 5,688 by trust here, #1,121 by citations
• Computer Vision and Pattern Recognition — 252 citations into it; #6,472 of 21,575 by trust here, #6,031 by citations
• Evaluation & Benchmarks — 238 citations into it; #1,222 of 3,147 by trust here, #1,728 by citations
• Representation & Self-Supervised Learning — 64 citations into it
• Model Architectures — 48 citations into it; #1,439 of 2,278 by trust here, #2,009 by citations
• Large Language Models — 44 citations into it
• Information Systems — 34 citations into it

Whom Mitesh M. Khapra trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Kaiming He
7. Yann LeCun
8. Ross B. Girshick
9. Jimmy Ba
10. Oriol Vinyals

Who trusts Mitesh M. Khapra overall

1. Ananya B. Sai
2. Shiyue Zhang
3. Xinya Du
4. Asli Celikyilmaz
5. Mohit Bansal
6. Claire Cardie
7. Purushotham Kamath
8. Angela Fan
9. Patrick Lewis
10. Tanay Dixit

Papers from Mitesh M. Khapra's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• An Autoencoder Approach to Learning Bilingual Word Representations (2014) — 348 citations
• Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph (2018) — 212 citations
• Towards Exploiting Background Knowledge for Building Conversation Systems (2018) — 174 citations
• Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks (2018) — 68 citations
• DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension (2018) — 206 citations
• Efficient Video Classification Using Fewer Frames (2019) — 94 citations
• PlotQA: Reasoning over Scientific Plots (2019) — 449 citations
• AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages (2020) — 116 citations
• A Survey of Evaluation Metrics Used for NLG Systems (2020) — 344 citations
• ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions (2018) — 31 citations
• Towards a Better Metric for Evaluating Question Generation Systems (2018) — 122 citations
• Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining (2020) — 83 citations
• Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model (2017) — 119 citations
• Perturbation CheckLists for Evaluating NLG Evaluation Metrics (2021) — 69 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisons (2022) — 10 citations
• A Primer on Pretrained Multilingual Language Models (2021) — 89 citations
• iNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages (2020) — 393 citations
• iNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages (2020) — 387 citations
• On Knowledge distillation from complex networks for response prediction (2019) — 16 citations