Diffusion models (DMs) have revolutionized generative learning. They utilize a diffusion process to encode data into a simple Gaussian distribution. However, encoding a complex, potentially multimodal data distribution into a single continuous Gaussian distribution arguably represents an unnecessarily challenging learning problem. We propose Discrete-Continuous Latent Variable Diffusion Models (DisCo-Diff) to simplify this task by introducing complementary discrete latent variables. We augment DMs with learnable discrete latents, inferred with an encoder, and train DM and encoder end-to-end. DisCo-Diff does not rely on pre-trained networks, making the framework universally applicable. The discrete latents significantly simplify learning the DM's complex noise-to-data mapping by reducing the curvature of the DM's generative ODE. An additional autoregressive transformer models the distribution of the discrete latents, a simple step because DisCo-Diff requires only few discrete variables with small codebooks. We validate DisCo-Diff on toy data, several image synthesis tasks as well as molecular docking, and find that introducing discrete latents consistently improves model performance. For example, DisCo-Diff achieves state-of-the-art FID scores on class-conditioned ImageNet-64/128 datasets with ODE sampler.
Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge
The Hong Kong Polytechnic University（香港理工大学）；Sichuan University（四川大学）；The Chinese University of Hong Kong（香港中文大学）；Hong Kong Baptist University（香港浸会大学）；NEOMA Business School（诺欧商学院）
Denoising diffusion probabilistic models (DDPMs) are a recent family of generative models that achieve state-of-the-art results. In order to obtain class-conditional generation, it was suggested to guide the diffusion process by gradients from a time-dependent classifier. While the idea is theoretically sound, deep learning-based classifiers are infamously susceptible to gradient-based adversarial attacks. Therefore, while traditional classifiers may achieve good accuracy scores, their gradients are possibly unreliable and might hinder the improvement of the generation results. Recent work dis…
Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
Quantum Measurement Group, MIT（麻省理工学院量子测量组）；Department of Materials Science and Engineering, MIT（麻省理工学院材料科学与工程系）；Department of Chemistry, MIT（麻省理工学院化学系）；Neutron Scattering Division, Oak Ridge National Laboratory（橡树岭国家实验室中子散射部）；Department of Chemistry, Michigan State University（密歇根州立大学化学系）；Department of Nuclear Science and Engineering, MIT（麻省理工学院核科学与工程系）
Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion
Enhancing material behavior discovery using embedding-oriented Physically-Guided Neural Networks with Internal Variables
Aragón Institute of Engineering Research (I3A)（阿拉贡工程研究所（I3A））；Mechanical Engineering Department（机械工程系）；University of Zaragoza（萨拉戈萨大学）；Institute for Health Research Aragon (IISA)（阿拉贡健康研究所（IISA））；Nanjing Tech University（南京工业大学）；Department of Mechanical Engineering, Universidad Politécnica de Madrid (UPM)（马德里理工大学机械工程系）
Understanding why machine learning algorithms may fail is usually the task of the human expert that uses domain knowledge and contextual information to discover systematic shortcomings in either the data or the algorithm. In this paper, we propose a semantic referee, which is able to extract qualitative features of the errors emerging from deep machine learning frameworks and suggest corrections. The semantic referee relies on ontological reasoning about spatial knowledge in order to characterize errors in terms of their spatial relations with the environment. Using semantics, the reasoner int…
University of Moratuwa（莫拉图瓦大学）；Harvard University（哈佛大学）；Aarhus University（奥胡斯大学）；Florida International University（佛罗里达国际大学）
Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, the performance of sleep staging based on ear-EEG is still inferior to scalp-EEG based sleep staging. In order to address the performance gap between scalp-EEG and ear-EEG based sleep staging, we propose a cross-modal knowledge distillation …
Brain computing interfaces (BCI) are used in a plethora of safety/privacy-critical applications, ranging from healthcare to smart communication and control. Wearable BCI setups typically involve a head-mounted sensor connected to a mobile device, combined with ML-based data processing. Consequently, they are susceptible to a multiplicity of attacks across the hardware, software, and networking stacks used that can leak users' brainwave data or at worst relinquish control of BCI-assisted devices to remote attackers. In this paper, we: (i) analyse the whole-system security and privacy threats to…
