Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Chen, Haoxian"'
Reinforcement Learning from human feedback (RLHF) has been shown a promising direction for aligning generative models with human intent and has also been explored in recent works for alignment of diffusion generative models. In this work, we provide
Externí odkaz:
http://arxiv.org/abs/2409.08400
Direct Preference Optimization (DPO) has recently emerged as a popular approach to improve reinforcement learning with human feedback (RLHF), leading to better techniques to fine-tune large language models (LLM). A weakness of DPO, however, lies in i
Externí odkaz:
http://arxiv.org/abs/2405.14953
Autor:
Chen, Haoxian, Lam, Henry
Bayesian Optimization is a popular approach for optimizing expensive black-box functions. Its key idea is to use a surrogate model to approximate the objective and, importantly, quantify the associated uncertainty that allows a sequential search of q
Externí odkaz:
http://arxiv.org/abs/2310.09766
Smart contracts manage a large number of digital assets nowadays. Bugs in these contracts have led to significant financial loss. Verifying the correctness of smart contracts is, therefore, an important task. This paper presents an automated safety v
Externí odkaz:
http://arxiv.org/abs/2211.14585
This paper presents DeCon, a declarative programming language for implementing smart contracts and specifying contract-level properties. Driven by the observation that smart contract operations and contract-level properties can be naturally expressed
Externí odkaz:
http://arxiv.org/abs/2207.13827
Autor:
Choromanski, Krzysztof, Chen, Haoxian, Lin, Han, Ma, Yuanzhe, Sehanobish, Arijit, Jain, Deepali, Ryoo, Michael S, Varley, Jake, Zeng, Andy, Likhosherstov, Valerii, Kalashnikov, Dmitry, Sindhwani, Vikas, Weller, Adrian
We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions
Externí odkaz:
http://arxiv.org/abs/2110.04367
Autor:
Choromanski, Krzysztof, Lin, Han, Chen, Haoxian, Zhang, Tianyi, Sehanobish, Arijit, Likhosherstov, Valerii, Parker-Holder, Jack, Sarlos, Tamas, Weller, Adrian, Weingarten, Thomas
In this paper we provide, to the best of our knowledge, the first comprehensive approach for incorporating various masking mechanisms into Transformers architectures in a scalable way. We show that recent results on linear causal attention (Choromans
Externí odkaz:
http://arxiv.org/abs/2107.07999
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability. To ensure accurate prediction, the model needs to be calibrated and validated against real data. Conventional methods approach these tasks
Externí odkaz:
http://arxiv.org/abs/2105.12893
We study the generation of prediction intervals in regression for uncertainty quantification. This task can be formalized as an empirical constrained optimization problem that minimizes the average interval width while maintaining the coverage accura
Externí odkaz:
http://arxiv.org/abs/2102.13625
Automated machine learning (AutoML) systems aim to enable training machine learning (ML) models for non-ML experts. A shortcoming of these systems is that when they fail to produce a model with high accuracy, the user has no path to improve the model
Externí odkaz:
http://arxiv.org/abs/2102.11267