Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Zhang, Ruqi"'
Autor:
Punyamoorty, Vineet, Jutras-Dubé, Pascal, Zhang, Ruqi, Aggarwal, Vaneet, Conover, Damon, Bera, Aniket
By framing reinforcement learning as a sequence modeling problem, recent work has enabled the use of generative models, such as diffusion models, for planning. While these models are effective in predicting long-horizon state trajectories in determin
Externí odkaz:
http://arxiv.org/abs/2409.16950
Planning with generative models has emerged as an effective decision-making paradigm across a wide range of domains, including reinforcement learning and autonomous navigation. While continuous replanning at each timestep might seem intuitive because
Externí odkaz:
http://arxiv.org/abs/2408.01510
Large language model (LLM) decoding involves generating a sequence of tokens based on a given context, where each token is predicted one at a time using the model's learned probabilities. The typical autoregressive decoding method requires a separate
Externí odkaz:
http://arxiv.org/abs/2407.12021
Aligning large language models (LLMs) with human preferences is critical for their deployment. Recently, decoding-time alignment has emerged as an effective plug-and-play technique that requires no fine-tuning of model parameters. However, generating
Externí odkaz:
http://arxiv.org/abs/2406.16306
Sparse training has emerged as a promising method for resource-efficient deep neural networks (DNNs) in real-world applications. However, the reliability of sparse models remains a crucial concern, particularly in detecting unknown out-of-distributio
Externí odkaz:
http://arxiv.org/abs/2403.20047
Discrete distributions, particularly in high-dimensional deep models, are often highly multimodal due to inherent discontinuities. While gradient-based discrete sampling has proven effective, it is susceptible to becoming trapped in local modes due t
Externí odkaz:
http://arxiv.org/abs/2402.17699
Publikováno v:
Published at International Conference on Learning Representations (ICLR) 2024
Bayesian neural networks (BNNs) offer uncertainty quantification but come with the downside of substantially increased training and inference costs. Sparse BNNs have been investigated for efficient inference, typically by either slowly introducing sp
Externí odkaz:
http://arxiv.org/abs/2402.11025
Autor:
Papamarkou, Theodore, Skoularidou, Maria, Palla, Konstantina, Aitchison, Laurence, Arbel, Julyan, Dunson, David, Filippone, Maurizio, Fortuin, Vincent, Hennig, Philipp, Hernández-Lobato, José Miguel, Hubin, Aliaksandr, Immer, Alexander, Karaletsos, Theofanis, Khan, Mohammad Emtiyaz, Kristiadi, Agustinus, Li, Yingzhen, Mandt, Stephan, Nemeth, Christopher, Osborne, Michael A., Rudner, Tim G. J., Rügamer, David, Teh, Yee Whye, Welling, Max, Wilson, Andrew Gordon, Zhang, Ruqi
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooke
Externí odkaz:
http://arxiv.org/abs/2402.00809
Low-precision training has emerged as a promising low-cost technique to enhance the training efficiency of deep neural networks without sacrificing much accuracy. Its Bayesian counterpart can further provide uncertainty quantification and improved ge
Externí odkaz:
http://arxiv.org/abs/2310.16320
Autor:
Li, Bolian, Zhang, Ruqi
Publikováno v:
ICLR 2024
Bayesian deep learning counts on the quality of posterior distribution estimation. However, the posterior of deep neural networks is highly multi-modal in nature, with local modes exhibiting varying generalization performance. Given a practical budge
Externí odkaz:
http://arxiv.org/abs/2310.05401