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pro vyhledávání: '"Chirag ."'
The present study proposes a new index to quantify the severity of non-stationary power quality (PQ) disturbance events. In particular, the severity of PQ events is estimated from their energy distribution in temporal-frequency space. The index essen
Externí odkaz:
http://arxiv.org/abs/2410.11442
Characterizing a quantum system by learning its state or evolution is a fundamental problem in quantum physics and learning theory with a myriad of applications. Recently, as a new approach to this problem, the task of agnostic state tomography was d
Externí odkaz:
http://arxiv.org/abs/2410.11957
Autor:
Setlur, Amrith, Nagpal, Chirag, Fisch, Adam, Geng, Xinyang, Eisenstein, Jacob, Agarwal, Rishabh, Agarwal, Alekh, Berant, Jonathan, Kumar, Aviral
A promising approach for improving reasoning in large language models is to use process reward models (PRMs). PRMs provide feedback at each step of a multi-step reasoning trace, potentially improving credit assignment over outcome reward models (ORMs
Externí odkaz:
http://arxiv.org/abs/2410.08146
The spatio-temporal complexity of video data presents significant challenges in tasks such as compression, generation, and inpainting. We present four key contributions to address the challenges of spatiotemporal video processing. First, we introduce
Externí odkaz:
http://arxiv.org/abs/2410.07659
Audio deepfake detection is crucial to combat the malicious use of AI-synthesized speech. Among many efforts undertaken by the community, the ASVspoof challenge has become one of the benchmarks to evaluate the generalizability and robustness of detec
Externí odkaz:
http://arxiv.org/abs/2410.07379
Some of the basic properties of any dynamical system can be summarized by a graph. The dynamical systems in our theory run from maps like the logistic map to ordinary differential equations to dissipative partial differential equations. We define two
Externí odkaz:
http://arxiv.org/abs/2410.05520
Autor:
Yadav, Shashank, Tomar, Rohan, Jain, Garvit, Ahooja, Chirag, Chaudhary, Shubham, Elkan, Charles
This paper introduces Gamified Adversarial Prompting (GAP), a framework that crowd-sources high-quality data for visual instruction tuning of large multimodal models. GAP transforms the data collection process into an engaging game, incentivizing pla
Externí odkaz:
http://arxiv.org/abs/2410.04038
Autor:
Leroux, Nathan, Manea, Paul-Philipp, Sudarshan, Chirag, Finkbeiner, Jan, Siegel, Sebastian, Strachan, John Paul, Neftci, Emre
Transformer neural networks, driven by self-attention mechanisms, are core components of foundational and Large Language Models. In generative transformers, self-attention uses cache memory to store token projections, avoiding recomputation at each t
Externí odkaz:
http://arxiv.org/abs/2409.19315
There has been a recent interest in understanding and characterizing the sample complexity of list learning tasks, where the learning algorithm is allowed to make a short list of $k$ predictions, and we simply require one of the predictions to be cor
Externí odkaz:
http://arxiv.org/abs/2409.19218
An emerging area of research aims to learn deep generative models with limited training data. Prior generative models like GANs and diffusion models require a lot of data to perform well, and their performance degrades when they are trained on only a
Externí odkaz:
http://arxiv.org/abs/2409.17439