While social media data is a valuable source for inferring human behavior, its in-practice utility hinges on extraneous factors. Notable is the “observer effect,” where awareness of being monitored can alter people’s social media use. We present a causal-inference study to examine this phenomenon on…
Existing research in measuring and mitigating gender bias predominantly centers on English, overlooking the intricate challenges posed by non-English languages and the Global South. This paper presents the first comprehensive study delving into the nuanced landscape of gender bias in Hindi, the third most spoken…
The opportunity for artificial intelligence, or AI, to enable accessibility is rapidly growing, but widely impactful applications can be challenging to build given the diversity of user need within and across disability communities. Teachable AI systems give users with disabilities a way to leverage the…
The field of automatic speech recognition (ASR) is now dominated by the end-to-end (E2E) models that directly map speech to text. In this talk, we will give an overview of the E2E ASR models and introduce the recent progress from an industry perspective. To design…
This repository is the source code for our paper: Federated Learning under Distributed Concept Drift (AISTATS'23).
Learning representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology, or epidemiology. Recent work embeds coordinates using sine and cosine projections based on Double Fourier Sphere (DFS) features. These embeddings assume…
This was an invite-only workshop. In recent years, large language models (LLMs) and artificial intelligence have made remarkable strides in a variety of tasks such as improving question answering, natural language understanding, and machine translation. These new technologies are now beginning to change the way…
LLMs rely on memory-intensive mechanisms like the key-value (KV) cache to store and quickly retrieve data. FastGen optimizes KV cache usage, reducing LLM memory demands by up to 50% while maintaining performance.
Self-correction is emerging as a promising approach to mitigate the issue of hallucination in Large Language Models (LLMs). To facilitate effective self-correction, recent research has proposed mistake detection as its initial step. However, current literature suggests that LLMs often struggle with reliably identifying reasoning mistakes…
Accurate and fast prediction of materials properties is central to the digital transformation of materials design. However, the vast design space and diverse operating conditions pose significant challenges for accurately modeling arbitrary material candidates and forecasting their properties. We present MatterSim, a deep learning model…
Precise, pixel-wise geolocalization of astronaut photography is critical to unlocking the potential of this unique type of remotely sensed Earth data, particularly for its use in disaster management and climate change research. Recent works have established the Astronaut Photography Localization task, but have either proved…
Language models are fundamentally changing how work gets done, and high-quality academic research is needed to ensure that the new future of work that they will help create is bright. Microsoft is soliciting proposals to fund research that will help shape the landscape of work…
