Pierre Liang

About

Pierre Liang is a Professor of Accounting at the Tepper School of Business at Carnegie Mellon University. His role involves teaching and research within the field of accounting, contributing to the academic community through his expertise. The Tepper School emphasizes experiential learning and practical application, preparing students to excel in their industries. As part of Carnegie Mellon University, Professor Liang is engaged in advancing knowledge in his field and supporting the school's strategic vision to lead at the intersection of business, technology, and analytics.

Research topics

• Artificial Intelligence
• Computer Science
• Psychology
• Machine Learning
• Natural Language Processing
• Computer Security
• Cognitive science
• Linguistics
• World Wide Web
• Database

Selected publications

• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

  arXiv (Cornell University) · 2022 · 548 citations

  Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyon…

  DOI

• Think Locally, Act Globally: Federated Learning with Local and Global\n Representations

  arXiv (Cornell University) · 2020 · 238 citations

  1st authorCorresponding

  Federated learning is a method of training models on private data distributed\nover multiple devices. To keep device data private, the global model is trained\nby only communicating parameters and updates which poses scalability challenges\nfor large models. To this end, we propose a new federated learning algorithm\nthat jointly learns compact local representations on each device and a global\nmodel across all devices. As a result, the global model can be smaller since it\nonly operates on loca…

  DOI

• Towards Debiasing Sentence Representations

  2020 · 149 citations

  1st authorCorresponding

  As natural language processing methods are increasingly deployed in real-world scenarios such as healthcare, legal systems, and social science, it becomes necessary to recognize the role they potentially play in shaping social biases and stereotypes. Previous work has revealed the presence of social biases in widely used word embeddings involving gender, race, religion, and other social constructs. While some methods were proposed to debias these word-level embeddings, there is a need to perform…

  DOI

• OpenFace 3.0: A Lightweight Multitask System for Comprehensive Facial Behavior Analysis

  ArXiv.org · 2025-06-03 · 2 citations

  preprintOpen access

  In recent years, there has been increasing interest in automatic facial behavior analysis systems from computing communities such as vision, multimodal interaction, robotics, and affective computing. Building upon the widespread utility of prior open-source facial analysis systems, we introduce OpenFace 3.0, an open-source toolkit capable of facial landmark detection, facial action unit detection, eye-gaze estimation, and facial emotion recognition. OpenFace 3.0 contributes a lightweight unified…

  PublisherOA PDFDOI

• Continuous First, Discrete Later: VQ-VAEs Without Dimensional Collapse

  arXiv (Cornell University) · 2026-05-07

  preprintOpen access

  While many approaches to improve VQ-VAE performance focus on codebook size and utilization, the effect of dimensional collapse, where trained VQ-VAE representations live in an extremely low-dimensional subspace (1-2% of full rank), remains unaddressed. We show theoretically and empirically that dimension collapse causes a hard loss lower bound that various codebook improvement techniques fail to surpass. Our analytic framework extends the sequential learning effect of Saxe et al. [2014] by intro…

  PublisherDOI

Frequent coauthors

• Louis‐Philippe Morency

  115 shared

• Ruslan Salakhutdinov

  59 shared

• Amir Zadeh

  Shenyang University of Technology

  32 shared

• Yiwei Lyu

  14 shared

• Ziyin Liu

  14 shared

• Yao-Hung Hubert Tsai

  12 shared

• Soujanya Poria

  9 shared

• Alex Wilf

  8 shared

Similar researchers at Carnegie Mellon University

• Denise Rousseauh-76
• Robert Millerh-67
• David Krackhardth-54