Suivre
Prateek Yadav
Titre
Citée par
Citée par
Année
Hypergcn: A new method for training graph convolutional networks on hypergraphs
N Yadati, M Nimishakavi, P Yadav, V Nitin, A Louis, P Talukdar
NeurIPS 2019, 1511-1522, 2019
5792019
TIES-merging: Resolving interference when merging models
P Yadav, D Tam, L Choshen, C Raffel, M Bansal
NeurIPS, 2024
266*2024
Self-Chained Image-Language Model for Video Localization and Question Answering
S Yu, J Cho, P Yadav, M Bansal
NeurIPS 2023, 2023
1452023
Graph convolutional networks based word embeddings
P Yadav, S Vashishth, M Bhandari, P Rai, C Bhattacharyya, P Talukdar
ACL 2019, 2018
138*2018
NHP: Neural hypergraph link prediction
N Yadati, V Nitin, M Nimishakavi, P Yadav, A Louis, P Talukdar
CIKM 2020, 1705-1714, 2020
127*2020
ExplaGraphs: An explanation graph generation task for structured commonsense reasoning
S Saha, P Yadav, L Bauer, M Bansal
EMNLP 2021, 2021
592021
Confidence-based graph convolutional networks for semi-supervised learning
S Vashishth, P Yadav, M Bhandari, P Talukdar
AISTATS 2019, 1792-1801, 2019
532019
Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
TY Zhuo, MC Vu, J Chim, H Hu, W Yu, R Widyasari, INB Yusuf, H Zhan, ...
arXiv preprint arXiv:2406.15877, 2024
502024
multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning
MB Swarnadeep Saha, Prateek Yadav
NAACL 2021, 2021
30*2021
Exploring continual learning for code generation models
P Yadav, Q Sun, H Ding, X Li, D Zhang, M Tan, X Ma, P Bhatia, ...
ACL 2023, 2023
272023
Pruning: Message Passing for Balancing Diversity & Difficulty in Data Pruning
A Maharana, P Yadav, M Bansal
The Twelfth International Conference on Learning Representations, 2024
25*2024
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy
P Li, Z Zhang, P Yadav, YL Sung, Y Cheng, M Bansal, T Chen
ICLR 2024 (Spotlight), 2023
222023
INSPIRE: A Framework for Integrating Individual User Preferences in Recourse
P Yadav, P Hase, M Bansal
TMLR, 2022
20*2022
Lovasz convolutional networks
P Yadav, M Nimishakavi, N Yadati, S Vashishth, A Rajkumar, P Talukdar
AISTATS 2019, 1978-1987, 2019
162019
A survey on model moerging: Recycling and routing among specialized experts for collaborative learning
P Yadav, C Raffel, M Muqeeth, L Caccia, H Liu, T Chen, M Bansal, ...
arXiv preprint arXiv:2408.07057, 2024
122024
Compeft: Compression for communicating parameter efficient updates via sparsification and quantization
P Yadav, L Choshen, C Raffel, M Bansal
arXiv preprint arXiv:2311.13171, 2023
102023
Explanation graph generation via pre-trained language models: An empirical study with contrastive learning
S Saha, P Yadav, M Bansal
ACL 2022, 2022
92022
Aurora-m: The first open source multilingual language model red-teamed according to the us executive order
T Nakamura, M Mishra, S Tedeschi, Y Chai, JT Stillerman, F Friedrich, ...
arXiv e-prints, arXiv: 2404.00399, 2024
82024
What Matters for Model Merging at Scale?
P Yadav, T Vu, J Lai, A Chronopoulou, M Faruqui, M Bansal, ...
arXiv preprint arXiv:2410.03617, 2024
62024
Llm merging: Building llms efficiently through merging
D Tam, M Li, P Yadav, RB Gabrielsson, J Zhu, K Greenewald, ...
NeurIPS 2024 Competition Track, 2024
42024
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