Tag: faang

If you play fantasy football, you are no stranger to the concept of data-driven decision making. Every week during football season, an estimated 60 million Americans pore over player statistics, point projections, and trade proposals, looking for that elusive insight that will guide their roster decisions and lead them to victory. But numbers only tell …

Read more “IBM Watson and ESPN use AI to transform fantasy football data into insight”

Collision avoidance is key for mobile robots and agents to operate safely in the real world. In this work, we present an efficient and effective collision avoidance system that combines real-world reinforcement learning (RL), search-based online trajectory planning, and automatic emergency intervention, e.g. automatic emergency braking (AEB). The goal of the RL is to learn …

Read more “Safe Real-World Reinforcement Learning for Mobile Agent Obstacle Avoidance”

The perception system in personalized mobile agents requires developing indoor scene understanding models, which can understand 3D geometries, capture objectiveness, analyze human behaviors, etc. Nonetheless, this direction has not been well-explored in comparison with models for outdoor environments (e.g., the autonomous driving system that includes pedestrian prediction, car detection, traffic sign recognition, etc.). In this …

Read more “Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents”

Estimating dimensional emotions, such as activation, valence and dominance, from acoustic speech signals has been widely explored over the past few years. While accurate estimation of activation and dominance from speech seem to be possible, the same for valence remains challenging. Previous research has shown that the use of lexical information can improve valence estimation …

Read more “Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation”

Pre-trained word embeddings, such as GloVe, have shown undesirable gender, racial, and religious biases. To address this problem, we propose DD-GloVe, a train-time debiasing algorithm to learn word embeddings by leveraging dictionary definitions. We introduce dictionary-guided loss functions that encourage word embeddings to be similar to their relatively neutral dictionary definition representations. Existing debiasing algorithms …

Read more “Learning Bias-reduced Word Embeddings Using Dictionary Definitions”

The matching principles behind optimal transport (OT) play an increasingly important role in machine learning, a trend which can be observed when OT is used to disambiguate datasets in applications (e.g. single-cell genomics) or used to improve more complex methods (e.g. balanced attention in transformers or self-supervised learning). To scale to more challenging problems, there …

Read more “Low-Rank Optimal Transport: Approximation, Statistics and Debiasing”