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Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing their relative performance on a large suite of tasks. Most published results on deep RL benchmarks compare point estimates of aggregate performance such as mean and median scores across tasks, ignoring the...

As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem. We introduce Mauve, a comparison measure for open-ended text generation, which directly compares the learnt distribution from a text generation...

The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.

Advancing deep learning techniques continue to demonstrate incredible potential to deliver exciting new AI-enhanced software and systems. But, training the most powerful models is expensive--financially, computationally, and environmentally. Increasing the efficiency of such models will have...

We are interested in learning generative models for complex geometries described via manifolds, such as spheres, tori, and other implicit surfaces. 
Current extensions of existing (Euclidean) generative models are restricted to specific geometries and typically suffer from high computational costs...

We introduce the "continuized" Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter. The two variables continuously mix following a linear ordinary differential equation and take gradient steps at random times. This continuized...

Benchmark datasets play a central role in the organization of machine learning research. They coordinate researchers around shared research problems and serve as a measure of progress towards shared goals. Despite the foundational role of benchmarking practices in this field, relatively little...

Computational methods that operate on three-dimensional (3D) molecular structure have the potential to solve important problems in biology and chemistry. Deep neural networks have gained significant attention, but their widespread adoption in the biomolecular domain has been limited by a lack of...

As your organization begins to consider building advanced deep learning models with efficiency in mind to improve the power delivered through your solutions, the software and hardware tools required for these implementations are foundational to achieving high-performance.

Our casual use of facial analysis tools can lead to more sinister applications

Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.

Deep learning (DL) systems have been gaining popularity in critical tasks such as credit evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL software implementations introduce variance: identical DL training runs (i.e., identical network, data, configuration...

This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs). In order to foster advances in responsible innovation, an in-depth understanding of the potential risks posed by these models is needed. A wide range of established and anticipated risks are...

We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document...

The author presents a step-by-step guide from annotation to training.

Computational modeling shows that both our ears and our environment influence how we hear.

The problem with machine translation: beware the wisdom of the crowd

This paper considers cooperative Multi-Agent Reinforcement Learning, focusing on emergent communication in settings where multiple pairs of independent learners interact at varying frequencies. In this context, multiple distinct and incompatible languages can emerge. When an agent encounters a...

Measurement update rules for Bayes filters often contain hand-crafted heuristics to compute observation probabilities for high-dimensional sensor data, like images. In this work, we propose the novel approach Deep Measurement Update (DMU) as a general update rule for a wide range of systems. DMU has...

We propose a paradigm shift in the data-driven modeling of the instrumental response field of telescopes. By adding a differentiable optical forward model into the modeling framework, we change the data-driven modeling space from the pixels to the wavefront. This allows to transfer a great deal of...