Spandana Gella

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 9 papers; trusts 579 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 260 citations into it; #19,896 of 35,345 by trust here, #11,750 by citations
• Natural Language Processing — 186 citations into it
• Computer Vision and Pattern Recognition — 150 citations into it; #16,635 of 21,575 by trust here, #9,079 by citations
• Evaluation & Benchmarks — 61 citations into it
• Multimodal Learning — 49 citations into it; #903 of 1,211 by trust here, #770 by citations
• Large Language Models — 47 citations into it; #576 of 799 by trust here, #751 by citations
• Safety, Robustness & Interpretability — 43 citations into it; #1,821 of 2,251 by trust here, #1,994 by citations
• Representation & Self-Supervised Learning — 22 citations into it; #2,542 of 3,145 by trust here, #2,330 by citations

Whom Spandana Gella trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ilya Sutskever
6. Alex Krizhevsky
7. Oriol Vinyals
8. Kaiming He
9. Yann LeCun
10. Andrew Zisserman

Who trusts Spandana Gella overall

1. Pranav Aggarwal
2. Douwe Kiela
3. Sebastian Ruder
4. David F. Harwath
5. Po-Yao (Bernie) Huang
6. Desmond Elliott
7. Haoyang Huang
8. Ivan Vulic
9. Ajinkya Kale
10. Grzegorz Chrupała

Papers from Spandana Gella's perspective

Ordered by how strongly the researchers Spandana Gella 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Image Pivoting for Learning Multilingual Multimodal Representations (2017) — 79 citations
• Multimodal Abstractive Summarization for How2 Videos (2019) — 110 citations
• Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings (2016) — 54 citations
• An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models (2020) — 207 citations
• Words Aren’t Enough, Their Order Matters: On the Robustness of Grounding Visual Referring Expressions (2020) — 62 citations
• Using In-Context Learning to Improve Dialogue Safety (2023) — 60 citations
• DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines (2022) — 6 citations
• Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generation (2021) — 41 citations
• A Dataset for Telling the Stories of Social Media Videos (2018) — 63 citations