Starting in January 2023:
New and Recent Events:
- Tutorial "Learning Deep
Low-Dim Models from High-Dim Data: From Theory to Practice", CVPR,
Seattle, June 17-21, 2024.
- BIRS Workshop "Mathematics of Deep
Learning", the Casa Matematica Oaxaca (CMO), Mexico, June 9-14,
2024.
- Invited talk on "Transparent and
Consistent Deep Representation Learning" at Alibaba Cloud, Hong Kong,
May 7, 2024.
- Tutorial "Building White-Box Deep Neural Networks",
ICASSP, Seoul, Korea, April 14-19, 2024.
- Guest of Honor and Speaker at
the 2024 Annual Joint High Table dinner, by the HKU student residence,
April 12, 2024.
- Talk on "Transparent and
Consistent Deep Representation Learning" at the College of Engineering
and Computer Science, VinUniversity, Hanoi, Vietnam, April
8th, 2024.
- Talk on "Transparent and
Consistent Deep Representation Learning" at the Department of
Statistics, Stanford University, March 7th, 2024.
- Recorded Talk on "Transparent and
Consistent Deep Representation Learning" at the Department of
Mathematics, UC Davis, March 6th, 2024.
- A Distinguished Lecture at the Masters Forum of the
Chinese University of Hong Kong, Shenzhen, January 16, 2024.
- A Tutorial Lecture on ReduNet at the International Conference on
Parsimony and Learning, Hong Kong, January 6, 2024.
- General Chair of the International Conference on
Parsimony and Learning, Hong Kong, January 3-6, 2024.
Recent Releases:
- A New Website: White-Box Transformers
via Sparse Rate Reduction.
- A New International Conference: Conference on Parsimony and
Learning (CPAL) (Hong Kong, Jan. 3-6, 2024).
- ACDL2023 Plenary Lectures on Deep Networks and
Intelligence.
- A New Textbook: High-Dimensional Data
Analysis with Low-Dimensional Models (or a mirror site
for download
in China).
- A New Position Paper:
On the Principles of Parsimony and Self-Consistency for the Emergence of Intelligence.
- A New Presentation & Roundtable Video: On Parsimony and Self-Consistency: the Origin and Nature of Intelligence.
- A New Tutorial: ICASSP 2023 Short Course on Low-dimensional Models
and Deep Networks (a seven-lecture short course).
- A New Course EECS208: Computational
Principles for High-Dimensional Data Analysis
(with a Course Website and Lecture Slides).
- Recorded video of talk: Transparent and
Consistent Deep Representation Learning, Mathematics of UC Davis,
March 6, 2024.
- Recorded videos on Youtube of From Artificial
Intelligence to Autonomous Inelligence, in Mandarin (with Slides), Harvard Academic Saloon, March 10, 2023.
- Recorded videos on Youtube of Tutorial and Lectures
of the 3rd SLowDNN Workshop, Abu Dhabi,
January 3-6, 2023.
- Recorded video on On the Principles of Parsimony and Self-Consistency:
Structured Compressive Closed-loop
Transcription, IDS HKU, Nov. 25, 2022.
- Recorded video on On Parsimony and Self-Consistency, the Origin and Nature of Intelligence
Workshop, BAAI, September 21, 2022.
- Recorded video on Closed-Loop Data Transcription
via Minimaxing Rate Reduction (with
Paper and Slides), Berkeley Neuroscience Redwood
Center, December 2, 2021.
- Recorded video on ReduNet: Deep
(Convolution) Networks from First Principles
(with Paper ), at CMSA of
Harvard University, April 16, 2021.
- Recorded video on Learning to Detect Geometric
Structures from Images, CVPR 3D Scene Understanding Workshop, June 19, 2021.
- Recorded video of An Overview of
Reinforcement Learning and Optimal Control (with Slides),
February 17, 2021.
Project Websites:
- Whitebox
Transformers via Sparse Rate Reduction (with
Yaodong Yu et. al.).
- ReduNet: Whitebox
Deep Networks
from the Principle of Rate Reduction (with Ryan Chan,
Yaodong Yu, Chong You, John Wright).
- Canonical Factors
for Hybrid Neural Fields (with Brent Yi, Weijia Zeng, and
Sam Buchanan).
- General In-hand Object
Rotation with Vision and Touch (with Haozhi Qi, Brent Yi, Jitendra Malik, etc.)
- Dexterous Robot
Hand Manipulation (with Haozhi Qi, Roberto Calandra, and
Jitendra Malik).
- Pursuit of
Large-Scale 3D Structures and Geometry (with Yichao Zhou,
Xili Dai, Haozhi Qi).
- UIUC/MSRA: Low-Rank Matrix Recovery
via Convex Optimization (with Wright, Lin and
Candes et. al.).
- UIUC: Face Recognition via
Sparse Representation (with Wright, Ganesh, Yang, Zhou
and Wagner et. al.).
- UIUC: Clustering and
Classification via Lossy Compression (with Wright Yang,
Mobahi, and Rao et. al.).
-
UIUC: Generalized Principal Component Analysis (with Huang and
Vidal).
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©2017 Yi Ma
Last modified: Sun Apr 7 10:33:22 HKT 2024
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University of California,
Berkeley 
