Zobrazeno 1 - 10
of 15 695
pro vyhledávání: '"., Surbhi"'
Autor:
Paliwal, Bhawna, Saini, Deepak, Dhawan, Mudit, Asokan, Siddarth, Natarajan, Nagarajan, Aggarwal, Surbhi, Malhotra, Pankaj, Jiao, Jian, Varma, Manik
Ranking a set of items based on their relevance to a given query is a core problem in search and recommendation. Transformer-based ranking models are the state-of-the-art approaches for such tasks, but they score each query-item independently, ignori
Externí odkaz:
http://arxiv.org/abs/2409.09795
Autor:
Madan, Surbhi, Ghosh, Shreya, Sookha, Lownish Rai, Ganaie, M. A., Subramanian, Ramanathan, Dhall, Abhinav, Gedeon, Tom
Estimating the Most Important Person (MIP) in any social event setup is a challenging problem mainly due to contextual complexity and scarcity of labeled data. Moreover, the causality aspects of MIP estimation are quite subjective and diverse. To thi
Externí odkaz:
http://arxiv.org/abs/2409.06224
This research investigates biases in text-to-image (TTI) models for the Indic languages widely spoken across India. It evaluates and compares the generative performance and cultural relevance of leading TTI models in these languages against their per
Externí odkaz:
http://arxiv.org/abs/2408.00283
Autor:
Akkiraju, Rama, Xu, Anbang, Bora, Deepak, Yu, Tan, An, Lu, Seth, Vishal, Shukla, Aaditya, Gundecha, Pritam, Mehta, Hridhay, Jha, Ashwin, Raj, Prithvi, Balasubramanian, Abhinav, Maram, Murali, Muthusamy, Guru, Annepally, Shivakesh Reddy, Knowles, Sidney, Du, Min, Burnett, Nick, Javiya, Sean, Marannan, Ashok, Kumari, Mamta, Jha, Surbhi, Dereszenski, Ethan, Chakraborty, Anupam, Ranjan, Subhash, Terfai, Amina, Surya, Anoop, Mercer, Tracey, Thanigachalam, Vinodh Kumar, Bar, Tamar, Krishnan, Sanjana, Kilaru, Samy, Jaksic, Jasmine, Algarici, Nave, Liberman, Jacob, Conway, Joey, Nayyar, Sonu, Boitano, Justin
Enterprise chatbots, powered by generative AI, are emerging as key applications to enhance employee productivity. Retrieval Augmented Generation (RAG), Large Language Models (LLMs), and orchestration frameworks like Langchain and Llamaindex are cruci
Externí odkaz:
http://arxiv.org/abs/2407.07858
We study how to subvert language models from following the rules. We model rule-following as inference in propositional Horn logic, a mathematical system in which rules have the form "if $P$ and $Q$, then $R$" for some propositions $P$, $Q$, and $R$.
Externí odkaz:
http://arxiv.org/abs/2407.00075
We study the problem of learning under arbitrary distribution shift, where the learner is trained on a labeled set from one distribution but evaluated on a different, potentially adversarially generated test distribution. We focus on two frameworks:
Externí odkaz:
http://arxiv.org/abs/2406.02742
Despite the success of Transformers on language understanding, code generation, and logical reasoning, they still fail to generalize over length on basic arithmetic tasks such as addition and multiplication. A major reason behind this failure is the
Externí odkaz:
http://arxiv.org/abs/2406.01895
Autor:
Surbhi, Venkataraman, Geetha
The power graph of a group $G$ is a graph with vertex set $G$, in which two vertices are adjacent if one is some power of the other. In the commuting graph, with $G$ as the vertex set, two vertices are joined by an edge if they commute in $G$. The en
Externí odkaz:
http://arxiv.org/abs/2406.00710
We study the stochastic bandit problem with ReLU neural network structure. We show that a $\tilde{O}(\sqrt{T})$ regret guarantee is achievable by considering bandits with one-layer ReLU neural networks; to the best of our knowledge, our work is the f
Externí odkaz:
http://arxiv.org/abs/2405.07331
Existing research often posits spurious features as easier to learn than core features in neural network optimization, but the impact of their relative simplicity remains under-explored. Moreover, studies mainly focus on end performance rather than t
Externí odkaz:
http://arxiv.org/abs/2403.03375