Architecture for Big Data & Machine Learning
Intel Domain Leader : Debbie Marr
Big Data Computing results in huge (and costly) energy consumption. Computing Technology energy is dominated by data movement. The research aims at new paradigms in handling data movements in Big Data environment consisting of minimizing data movement, moving the computing closer to the data, accelerating the computing, and exploiting more effective novel memory technologies.
The projects
Conversational Speech Understanding
Intel Domain Leader : Moshe Wasserllat
The Converstaional Speech Understanding technology deals with understanding, analyzing and extracting valuable insight from human-to-human, verbal and/or textual interactions (e.g. meetings). Unlike human-to-machine existing solutions (e.g. SIRI), the challenges induced by Conversational Understanding are currently unaddressed by the industry. It is a generic technology that can enable multiple capabilities critical to rising usages (e.g. Meeting Assistants, Business Analytics, Customers Experience etc…)
The key developments will include: Integrated Speech and Text understanding, Natural Language knowledge Graph representation, Personal user modeling (e.g. behavioral patterns), Events and relations extraction and discourse analysis (e.g. argumentation & deliberation)…
The projects
- Universal Semantics (UCCA)
- Automatic Measurement of Transcription Quality
- Holistic Inference for Natural Language Processing
- Open Information Extraction Knowledge Graphs
- Unsupervised Extraction of Relations and Events
- Hybrid Models for Minimally Supervised Information Extraction from Conversations
- Syntactic and Semantic Reranking of Speech Interaction Data
- Topic Dependent Language Modeling
- Providing People with Arguments during Persuasive Discussion
Distributed Open Deep Learning Library for IA
Intel Domain Leader: Shai Fine
We plan to develop open-source library for large scale distributed training of deep networks, which is:
1) optimized for IA (Xeon, Xeon-Phi),
2) based on open-source data analytics cluster computing framework (Spark, Hadoop).
The research project will:
(1) Employ advanced ML concepts such as distributed learning and improved deep learning architecture.
(2) Include new, advanced & demanding, deep learning based use cases.
The projects
- Optimal Deep Learning and the Information Bottleneck Principle
- SimNets: A Generalization of Convolutional Networks
- Rigorous Algorithms for Distributed Deep Learning
- Mega-Class Efficient Deep Learning
- Outlier Robust Distributed Learning + Learning Deep Forward Models for Reinforcement Learning
- Unsupervised and Semi-supervised Ensemble Learning
- Distributed Deep Learning on Xeon-Phi
- Distributed Methods for Non-Convex and Deep Learning
- Scene Understanding: from Image to Text and from Image and a Question to an Answer
- Applications of Deep Learning to Medical Imaging
- Image Restoration using Deep Learning
Visual Processing and Understating
Intel Domain Leader – Ronny Ronen
In addition to the ICRI-CI 3-layered capstone research, ICRI-CI host several research project in the visual processing and understanding domain. These research projects were added because of their potential high value to Intel, the industry, or the academia.
- Saliency estimation in video
- Statistics of depth images
- Mental phenotyping from 3D cameras
- Blind Video: Video without photographers
Past Projects
Advanced Machine Learning
Novel Heterogeneous Computing Platforms
- Heterogeneous Computing Platforms
- Accelerators for Massive Memory Parallel Machine Learning and Applications
- Machine Learning for Architecture: Self-Learning, Predictive, Computer Systems
Cognition
- Language Understanding Using Local Patterns
- Mobile Sensing for User Activity and Context
- Providing People with Arguments and Contextual Information during Argumentative
- Open-IE Knowledge Graphs
Learning Visual Systems
- Understanding and Utilizing Natural Image Statistics
- Large Scale Perceptual Summaries of Visual Information
- Brain-Inspired Sparse Representations for Visual Recognition
- Scene Understanding using Deep Learning Tools
Brain-inspired computing
- Dynamic self-configurable architectures for intelligent learning agents
- Hardware Neural Network Accelerators
Intelligent Agents