Khyathi Raghavi Chandu
Artificial Intelligence · 2021–2024
A researcher in the Science corpus: 7 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 286 citations into it
- Computer Vision and Pattern Recognition — 76 citations into it
- Model Architectures — 11 citations into it
- Computer Networks and Communications — 8 citations into it
- Hardware and Architecture — 8 citations into it
Whom Khyathi Raghavi Chandu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Papers from Khyathi Raghavi Chandu's perspective
Ordered by how strongly the researchers Khyathi Raghavi Chandu trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Language Models are Few-Shot Learners (Neural Information Processing Systems)
- Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Journal of machine learning research)
Their papers
- The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021) — 329 citations
- How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources (2023) — 510 citations
- GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022) — 43 citations
- OLMo: Accelerating the Science of Language Models (2024) — 700 citations
- Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research (2024) — 519 citations
- The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning (2023) — 325 citations
- RewardBench: Evaluating Reward Models for Language Modeling (2024) — 450 citations