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Understanding Language Representations in Deep Learning Models

Our goal is to explore language representations in computational models. We develop new models for representing natural language and investigate how existing models learn language, focusing on neural network models in key tasks like machine translation and speech recognition.

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3D Generative Adversarial Networks

We study the problem of 3D object generation. We propose a novel framework, 3D Generative Adversarial Network (3D-GAN), leveraging recent advances in volumetric convolutional networks and generative adversarial nets.

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PrivacyML - A Privacy Preserving Framework for Machine Learning

We are developing a general framework that enforces privacy transparently enabling different kinds of machine learning to be developed that are automatically privacy preserving.

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Unsupervised Speech Processing

All humans process vast quantities of unannotated speech and manage to learn phonetic inventories, word boundaries, etc., and can use these abilities to acquire new word. Why can't ASR technology have similar capabilities? Our goal in this research project is to build speech technology using unannotated speech corpora.

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Unsupervised Learning of Interpretable Representations from Sequential Data

Generation of sequential data involves multiple factors operating at different temporal scales. Take natural speech for example, the speaker identity tends to be consistent within an utterance, while the phonetic content changes from frame to frame. By explicitly modeling such hierarchical generative process under a probabilistic framework, we proposed a model that learns to factorizes sequence-level factors and sub-sequence-level factors into different sets of representations without any supervision.

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Geometry in Large-Scale Machine Learning

Data often has geometric structure which can enable better inference; this project aims to scale up geometry-aware techniques for use in machine learning settings with lots of data, so that this structure may be utilized in practice.

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The Car Can Explain!

Developing techniques to allow self-driving cars and other AI-driven systems to explain behaviors and failures.

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Using Computers to Eat Healthier

Our goal is to build an AI-powered personal digital nutritionist that enables users to track the food they eat simply by speaking or typing natural English phrases.

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SoFi - The Soft Robotic Fish

Exploration of underwater life with an acoustically controlled soft robotic fish

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Sensible Deep Learning for 3D Data

Developing state-of-the-art deep learning algorithms for analyzing and modeling 3D geometry
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MIT CSAIL

Massachusetts Institute of Technology

Computer Science & Artificial Intelligence Laboratory

32 Vassar St, Cambridge MA 02139

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MIT Schwarzman College of Computing