Zobrazeno 1 - 10
of 138 254
pro vyhledávání: '"Shankar, A."'
We propose a novel parameter-efficient training (PET) method for large language models that adapts models to downstream tasks by optimizing a small subset of the existing model parameters. Unlike prior methods, this subset is not fixed in location bu
Externí odkaz:
http://arxiv.org/abs/2411.08610
The Nash equilibrium (NE) is fundamental game-theoretic concept for characterizing stability in static strategic form games. However, at times, NE fails to capture outcomes in dynamic settings, where players' actions evolve over time in response to o
Externí odkaz:
http://arxiv.org/abs/2411.08471
Autor:
Dai, Wei, Liu, Gangqiang, Joshi, Vidul, Miano, Alessandro, Sivak, Volodymyr, Shankar, Shyam, Devoret, Michel H.
Josephson element-based parametric amplifiers (JPAs) typically require rf pump power that is several orders of magnitude stronger than the maximum signal power they can handle. The low power efficiency and strong pump leakage towards signal circuitry
Externí odkaz:
http://arxiv.org/abs/2411.07208
Autor:
Shah, Aayush, Jayaratnam, Shankar
Large language models (LLMs) have demonstrated significant success in natural language processing (NLP) tasks and have shown promising results in other domains such as protein sequence generation. However, there remain salient differences between LLM
Externí odkaz:
http://arxiv.org/abs/2411.05966
Autor:
Ding, Jianqiang, Deka, Shankar A.
In this work, we present a novel Koopman spectrum-based reachability verification method for nonlinear systems. Contrary to conventional methods that focus on characterizing all potential states of a dynamical system over a presupposed time span, our
Externí odkaz:
http://arxiv.org/abs/2411.05554
Autor:
Wolff, Malcolm, Olivares, Kin G., Oreshkin, Boris, Ruan, Sunny, Yang, Sitan, Katoch, Abhinav, Ramasubramanian, Shankar, Zhang, Youxin, Mahoney, Michael W., Efimov, Dmitry, Quenneville-Bélair, Vincent
Publikováno v:
In 31st Conference on Neural Information Processing In 38th Conference on Neural Information Processing Systems NIPS 2017, Time Series in the Age of Large Models Workshop, 2024
Demand forecasting faces challenges induced by Peak Events (PEs) corresponding to special periods such as promotions and holidays. Peak events create significant spikes in demand followed by demand ramp down periods. Neural networks like MQCNN and MQ
Externí odkaz:
http://arxiv.org/abs/2411.05852
Autor:
Maheshwari, Chinmay, Mendoza, Maria G., Tuck, Victoria Marie, Su, Pan-Yang, Qin, Victor L., Seshia, Sanjit A., Balakrishnan, Hamsa, Sastry, Shankar
Advanced Air Mobility (AAM) operations are expected to transform air transportation while challenging current air traffic management practices. By introducing a novel market-based mechanism, we address the problem of on-demand allocation of capacity-
Externí odkaz:
http://arxiv.org/abs/2411.03582
We present numerical results for the effects of influence by high-amplitude periodic pulse series on a network of nonlocally coupled Hindmarsh-Rose neurons with 2D geometry of the topology. We consider the case when the pulse amplitude is larger than
Externí odkaz:
http://arxiv.org/abs/2411.02130
Incremental stability of dynamical systems ensures the convergence of trajectories from different initial conditions towards each other rather than a fixed trajectory or equilibrium point. Here, we introduce and characterize a novel class of incremen
Externí odkaz:
http://arxiv.org/abs/2411.01872
Autor:
Viswanathan, Sruthi, Ibrahim, Seray, Shankar, Ravi, Binns, Reuben, Van Kleek, Max, Slovak, Petr
Parenting brings emotional and physical challenges, from balancing work, childcare, and finances to coping with exhaustion and limited personal time. Yet, one in three parents never seek support. AI systems potentially offer stigma-free, accessible,
Externí odkaz:
http://arxiv.org/abs/2411.01228