Advancing the state of the art

Graph embeddings are a ubiquitous tool for machine learning tasks on graph-structured data (e.g., node classification and link prediction). Computing embeddings for large-scale graphs, however, is often prohibitively inefficient, even if we are only interested in a small subset of relevant vertices. To address this, we present an efficient graph coarsening algorithm based on Schur complements...

Watermarking is the process of embedding information into an image that can survive under distortions, while requiring the encoded image to have little or no perceptual difference with the original image. Recently, deep learning-based methods achieved impressive results in both visual quality and message payload under a wide variety of image distortions. However, these methods all require...

Dynamic mechanism design expands the scope on what type of allocation rules can be implemented andwhat revenue can be extracted when compared to static mechanisms. The case of separable environments(i.e. one player can be de-allocated while keeping the allocation of the remaining players intact) is verywell understood. The mechanisms in the literature don’t extend to the non-separable...

There is a recent surge of papers that focus on attention as explanation of model predictions, giving mixed evidence on whether attention can be used as such. This has led some to try and `improve' attention so as to make it more interpretable. We argue that we should pay attention no heed. While attention conveniently gives us one weight per input token and is easily extracted, it is often...

Neural text generation (data- or text-to-text) demonstrates remarkable performance when training data is abundant which for many applications is not the case. To collect a large corpus of parallel data, heuristic rules are often used but they inevitably let noise into the data, such as phrases in the output which cannot be explained by the input. Consequently, models pick up on the noise and...

Latency is a crucial metric for streaming speech recognition systems. In this paper, we reduce latency by fetching responses early based on the partial recognition results and refer to it as prefetching. Specifically, prefetching works by submitting partial recognition results for subsequent processing such as obtaining assistant server responses or second-pass rescoring before the recognition...

The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years.But both of these problems suffer from relatively small training sets, creating the need for statistically efficient methods to learn 3D shape representations. In this work, we investigate the use of Approximate Convex Decompositions (ACD) as a self-...