Evaluating machine learning models requires evaluation data.

It is largely agreed that social network links are formed due to either homophily or social influence. Inspired by this, we aim at understanding the generation of links via providing a novel embedding-based graph formation model.

Overcoming this challenge, existing object hallucination evaluation methods average the results obtained from a set of instructions. However, these methods fail to provide consistent evaluation across instruction sets that generate image descriptions of significantly different lengths.

Multi-task learning is a promising paradigm for handling several tasks simultaneously using a unified architecture.

Predicting and constructing road geometric information ( e.g ., lane lines, road markers) is a crucial task for safe autonomous driving, while such static map

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Classical worst-case optimization theory neither explains the success of optimization in machine learning, nor does it help with step size selection.

Scene flow estimation is an essential ingredient for a variety of real-world applications, especially for autonomous agents, such as self-driving cars and robots.

However, we observe that how to select samples for labelling also significantly impacts performance, particularly under extremely low-budget settings. The sample selection task in SSL has been under-explored for a long time.

We demonstrate FlexCap's effectiveness in several applications: first, it achieves strong performance in dense captioning tasks on the Visual Genome dataset. Second, we show how FlexCap's localized