Zobrazeno 1 - 10
of 9 174
pro vyhledávání: '"Pushkar' IS"'
While X-ray imaging is indispensable in medical diagnostics, it inherently carries with it those noises and limitations on resolution that mask the details necessary for diagnosis. B/W X-ray images require a careful balance between noise suppression
Externí odkaz:
http://arxiv.org/abs/2411.12833
Recent advancements in session-based recommendation models using deep learning techniques have demonstrated significant performance improvements. While they can enhance model sophistication and improve the relevance of recommendations, they also make
Externí odkaz:
http://arxiv.org/abs/2411.09152
Autor:
Koeplinger, David, Gandhi, Darshan, Nandkar, Pushkar, Sheeley, Nathan, Musaddiq, Matheen, Zhang, Leon, Goodbar, Reid, Shaffer, Matthew, Wang, Han, Wang, Angela, Wang, Mingran, Prabhakar, Raghu
Token generation speed is critical to power the next wave of AI inference applications. GPUs significantly underperform during token generation due to synchronization overheads at kernel boundaries, utilizing only 21% of their peak memory bandwidth.
Externí odkaz:
http://arxiv.org/abs/2410.23668
Autor:
Rastogi, Charvi, Teh, Tian Huey, Mishra, Pushkar, Patel, Roma, Ashwood, Zoe, Davani, Aida Mostafazadeh, Diaz, Mark, Paganini, Michela, Parrish, Alicia, Wang, Ding, Prabhakaran, Vinodkumar, Aroyo, Lora, Rieser, Verena
AI systems crucially rely on human ratings, but these ratings are often aggregated, obscuring the inherent diversity of perspectives in real-world phenomenon. This is particularly concerning when evaluating the safety of generative AI, where percepti
Externí odkaz:
http://arxiv.org/abs/2410.17032
This paper introduces misinfo-general, a benchmark dataset for evaluating misinformation models' ability to perform out-of-distribution generalisation. Misinformation changes rapidly, much quicker than moderators can annotate at scale, resulting in a
Externí odkaz:
http://arxiv.org/abs/2410.18122
Autor:
Chundawat, Vikram S, Niroula, Pushkar, Dhungana, Prasanna, Schoepf, Stefan, Mandal, Murari, Brintrup, Alexandra
Federated learning (FL) has enabled collaborative model training across decentralized data sources or clients. While adding new participants to a shared model does not pose great technical hurdles, the removal of a participant and their related infor
Externí odkaz:
http://arxiv.org/abs/2410.04144
Carroll hydrodynamics arises in the $c\to 0$ limit of relativistic hydrodynamics. Instances of its relevance include the Bjorken and Gubser flow models of heavy-ion collisions, where the ultrarelativistic nature of the flow makes the physics effectiv
Externí odkaz:
http://arxiv.org/abs/2409.18763
Autor:
Pushkar, Arnav, Kumar, Brijesh
The electronic specific heat of Kondo insulators in magnetic field is studied for the half-filled Kondo lattice model on simple cubic lattice using a low-temperature theory in Kumar representation. The calculated specific heat is found to show quantu
Externí odkaz:
http://arxiv.org/abs/2408.15216
Autor:
Jajoria, Pushkar, McDermott, James
This study introduces a text-conditioned approach to generating drumbeats with Latent Diffusion Models (LDMs). It uses informative conditioning text extracted from training data filenames. By pretraining a text and drumbeat encoder through contrastiv
Externí odkaz:
http://arxiv.org/abs/2408.02711
We introduce null contractions of the Poincare and relativistic conformal algebras. The longitudinal null contraction involves writing the algebra in lightcone coordinates and contracting one of the null directions. For the Poincare algebra, this yie
Externí odkaz:
http://arxiv.org/abs/2406.15061