Distributed Extra-Gradient With Optimal Complexity And Communication Guarantees

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In a typical fusion experiment, the plasma can have several possible confinement modes. At the tokamak a configuration variable, aside from the low (L) and high (H) confinement modes, an additional mode, dithering (D), is frequently observed. Developing me ...

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks sti ...

The goal of out-of-distribution (OoD) detection is to identify unseen categories of inputs different from those seen during training, which is an important requirement for the safe deployment of deep neural networks in computational pathology. Additionally ...

Artificial intelligence has been an ultimate design goal since the inception of computers decades ago. Among the many attempts towards general artificial intelligence, modern machine learning successfully tackles many complex problems thanks to the progres ...

Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often limited to a fixed set of hard-coded transformations. Recently, several work ...

We propose to leverage Transformer architectures for non-autoregressive human motion prediction. Our approach decodes elements in parallel from a query sequence, instead of conditioning on previous predictions such as in state-of-the-art RNN-based approach ...

Distributed Extra-Gradient With Optimal Complexity And Communication Guarantees

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Plasma confinement mode classification using a sequence-to-sequence neural network with attention

Social NCE: Contrastive Learning of Socially-aware Motion Representations

SOoD: Self-Supervised Out-of-Distribution Detection Under Domain Shift for Multi-Class Colorectal Cancer Tissue Types

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

Semantic Perturbations with Normalizing Flows for Improved Generalization

Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers