Zobrazeno 1 - 10
of 26 002
pro vyhledávání: '"A Bharadwaj"'
Prompt-Tuning is an efficient method for adapting pre-trained language models to new tasks with minimal computational overhead by modifying prompt embeddings. In this work, we investigate how crucial the phenomenon of embedding collapse, frequently o
Externí odkaz:
http://arxiv.org/abs/2412.18582
Autor:
Ravichandran, Bharadwaj, Lynch, Alexander, Brockman, Sarah, RichardWebster, Brandon, Du, Dawei, Hoogs, Anthony, Funk, Christopher
Both few-shot learning and domain adaptation sub-fields in Computer Vision have seen significant recent progress in terms of the availability of state-of-the-art algorithms and datasets. Frameworks have been developed for each sub-field; however, bui
Externí odkaz:
http://arxiv.org/abs/2412.16275
Autor:
Bharadwaj, Praveen, Kumar, Ranjeet, Prajapati, Hemant Kumar, Srivastava, Rahul, Yadav, Sushant
The current generation of Dark Matter Direct Detection Experiments has ruled out a large region of parameter space for dark matter, particularly in the ($10 - 1000$) GeV mass range. However, due to very low event rates, searching for dark matter in t
Externí odkaz:
http://arxiv.org/abs/2412.13301
Manipulating the illumination within a single image represents a fundamental challenge in computer vision and graphics. This problem has been traditionally addressed using inverse rendering techniques, which require explicit 3D asset reconstruction a
Externí odkaz:
http://arxiv.org/abs/2412.11224
Autor:
Wu, Qilong, Xiang, Xiaoneng, Huang, Hejia, Wang, Xuan, Jie, Yeo Wei, Satapathy, Ranjan, Filho, Ricardo Shirota, Veeravalli, Bharadwaj
The rapid growth of the financial sector and the rising focus on Environmental, Social, and Governance (ESG) considerations highlight the need for advanced NLP tools. However, open-source LLMs proficient in both finance and ESG domains remain scarce.
Externí odkaz:
http://arxiv.org/abs/2412.10906
Autor:
Nagrani, Arsha, Zhang, Mingda, Mehran, Ramin, Hornung, Rachel, Gundavarapu, Nitesh Bharadwaj, Jha, Nilpa, Myers, Austin, Zhou, Xingyi, Gong, Boqing, Schmid, Cordelia, Sirotenko, Mikhail, Zhu, Yukun, Weyand, Tobias
This paper describes a semi-automatic pipeline to generate challenging question-answer-decoy sets for understanding long videos. Many existing video datasets and models are focused on short clips (10s-30s). While some long video datasets do exist, th
Externí odkaz:
http://arxiv.org/abs/2412.09582
Autor:
Kang, Hao, Bharadwaj, Srikant, Hensman, James, Krishna, Tushar, Ruhle, Victor, Rajmohan, Saravan
Large language model (LLM) inference demands significant amount of computation and memory, especially in the key attention mechanism. While techniques, such as quantization and acceleration algorithms, like FlashAttention, have improved efficiency of
Externí odkaz:
http://arxiv.org/abs/2412.08585
Automatic syllable stress detection is a crucial component in Computer-Assisted Language Learning (CALL) systems for language learners. Current stress detection models are typically trained on clean speech, which may not be robust in real-world scena
Externí odkaz:
http://arxiv.org/abs/2412.08306
Autor:
Mondal, Anindita, Bharadwaj, Rangavajjala Sankara, Mallela, Jhansi, Vuppala, Anil Kumar, Yarra, Chiranjeevi
Automatic detection of prominence at the word and syllable-levels is critical for building computer-assisted language learning systems. It has been shown that prosody embeddings learned by the current state-of-the-art (SOTA) text-to-speech (TTS) syst
Externí odkaz:
http://arxiv.org/abs/2412.08283
Chain-of-Thought (CoT) prompting has significantly enhanced the reasoning abilities of large language models. However, recent studies have shown that models can still perform complex reasoning tasks even when the CoT is replaced with filler(hidden) c
Externí odkaz:
http://arxiv.org/abs/2412.04537