1 code implementation • 8 May 2024 • Shengyang Sun, Xiaojin Gong
In the pursuit of effective multimodal violence detection (MVD), information redundancy, modality imbalance, and modality asynchrony are identified as three key challenges.
1 code implementation • 2 May 2024 • Gerald Shen, Zhilin Wang, Olivier Delalleau, Jiaqi Zeng, Yi Dong, Daniel Egert, Shengyang Sun, Jimmy Zhang, Sahil Jain, Ali Taghibakhshi, Markel Sanz Ausin, Ashwath Aithal, Oleksii Kuchaiev
However, building efficient tools to perform alignment can be challenging, especially for the largest and most competent LLMs which often contain tens or hundreds of billions of parameters.
1 code implementation • 31 Mar 2023 • Shengyang Sun, Xiaojin Gong
That is, clip-level pseudo labels generated from each network are used to supervise the other one at the next training round, and the two networks are learned alternatively and iteratively.
no code implementations • CVPR 2023 • Shengyang Sun, Xiaojin Gong
In this work, we propose a hierarchical semantic contrast (HSC) method to learn a scene-aware VAD model from normal videos.
no code implementations • ICLR 2022 • Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis Titsias
A challenging problem in task-free continual learning is the online selection of a representative replay memory from data streams.
no code implementations • ICLR 2022 • Jimmy Ba, Murat A Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang
Stein variational gradient descent (SVGD) is a deterministic inference algorithm that evolves a set of particles to fit a target distribution.
2 code implementations • 10 Jun 2021 • Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger Grosse
We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability.
no code implementations • pproximateinference AABI Symposium 2021 • Shengyang Sun, Jiaxin Shi, Roger Baker Grosse
Equivalences between infinite neural networks and Gaussian processes have been established for explaining the functional prior and training dynamics of deep learning models.
1 code implementation • 6 Nov 2020 • Chaoqi Wang, Shengyang Sun, Roger Grosse
While uncertainty estimation is a well-studied topic in deep learning, most such work focuses on marginal uncertainty estimates, i. e. the predictive mean and variance at individual input locations.
no code implementations • pproximateinference AABI Symposium 2019 • Jimmy Ba, Murat A. Erdogdu, Marzyeh Ghassemi, Taiji Suzuki, Shengyang Sun, Denny Wu, Tianzong Zhang
Particle-based inference algorithm is a promising method to efficiently generate samples for an intractable target distribution by iteratively updating a set of particles.
no code implementations • NeurIPS 2019 • Jun Yang, Shengyang Sun, Daniel M. Roy
The developments of Rademacher complexity and PAC-Bayesian theory have been largely independent.
3 code implementations • ICLR 2019 • Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger Grosse
We introduce functional variational Bayesian neural networks (fBNNs), which maximize an Evidence Lower BOund (ELBO) defined directly on stochastic processes, i. e. distributions over functions.
4 code implementations • ICML 2018 • Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger Grosse
The NKN architecture is based on the composition rules for kernels, so that each unit of the network corresponds to a valid kernel.
3 code implementations • ICML 2018 • Jiaxin Shi, Shengyang Sun, Jun Zhu
Recently there have been increasing interests in learning and inference with implicit distributions (i. e., distributions without tractable densities).
1 code implementation • ICLR 2019 • James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse
Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed along low curvature directions.
2 code implementations • ICML 2018 • Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
Variational Bayesian neural nets combine the flexibility of deep learning with Bayesian uncertainty estimation.
1 code implementation • 18 Sep 2017 • Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, Yuhao Zhou
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.
no code implementations • ICLR 2018 • Jiaxin Shi, Shengyang Sun, Jun Zhu
Recent progress in variational inference has paid much attention to the flexibility of variational posteriors.