Zobrazeno 1 - 10
of 1 044
pro vyhledávání: '"P. Bafna"'
Autor:
Bafna, Mitali, Minzer, Dor
In the almost-everywhere reliable message transmission problem, introduced by [Dwork, Pippenger, Peleg, Upfal'86], the goal is to design a sparse communication network $G$ that supports efficient, fault-tolerant protocols for interactions between all
Externí odkaz:
http://arxiv.org/abs/2501.00337
Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures
We develop a new approach for clustering non-spherical (i.e., arbitrary component covariances) Gaussian mixture models via a subroutine, based on the sum-of-squares method, that finds a low-dimensional separation-preserving projection of the input da
Externí odkaz:
http://arxiv.org/abs/2411.12438
In today's day and age where information is rapidly spread through online platforms, the rise of fake news poses an alarming threat to the integrity of public discourse, societal trust, and reputed news sources. Classical machine learning and Transfo
Externí odkaz:
http://arxiv.org/abs/2410.09455
Autor:
Mathur, Suyash Vardhan, Bafna, Jainit Sushil, Kartik, Kunal, Khandelwal, Harshita, Shrivastava, Manish, Gupta, Vivek, Bansal, Mohit, Roth, Dan
Existing datasets for tabular question answering typically focus exclusively on text within cells. However, real-world data is inherently multimodal, often blending images such as symbols, faces, icons, patterns, and charts with textual content in ta
Externí odkaz:
http://arxiv.org/abs/2408.13860
We construct 2-query, quasi-linear size probabilistically checkable proofs (PCPs) with arbitrarily small constant soundness, improving upon Dinur's 2-query quasi-linear size PCPs with soundness $1-\Omega(1)$. As an immediate corollary, we get that un
Externí odkaz:
http://arxiv.org/abs/2407.12762
Large Language Models (LLMs) have showcased impressive abilities in generating fluent responses to diverse user queries. However, concerns regarding the potential misuse of such texts in journalism, educational, and academic contexts have surfaced. S
Externí odkaz:
http://arxiv.org/abs/2407.02978
While large language models exhibit certain cross-lingual generalization capabilities, they suffer from performance degradation (PD) on unseen closely-related languages (CRLs) and dialects relative to their high-resource language neighbour (HRLN). Ho
Externí odkaz:
http://arxiv.org/abs/2406.13718
We develop a new approach for approximating large independent sets when the input graph is a one-sided spectral expander - that is, the uniform random walk matrix of the graph has its second eigenvalue bounded away from 1. Consequently, we obtain a p
Externí odkaz:
http://arxiv.org/abs/2405.10238
While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. In the context of low-resource (LR) MT between two closely-related lang
Externí odkaz:
http://arxiv.org/abs/2403.10963
Autor:
Sandilya, Harshit, Raj, Peehu, Bafna, Jainit Sushil, Mukhopadhyay, Srija, Sharma, Shivansh, Sharma, Ellwil, Sharma, Arastu, Trivedi, Neeta, Shrivastava, Manish, Kumar, Rajesh
Large language models (LLMs) often struggle with complex mathematical tasks, prone to "hallucinating" incorrect answers due to their reliance on statistical patterns. This limitation is further amplified in average Small LangSLMs with limited context
Externí odkaz:
http://arxiv.org/abs/2402.12080