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Intel Labs Presents 31 Papers at NeurIPS 2023

This year, Intel Labs presents 31 papers at NeurIPS, including 12 at the main conference. Contributi...
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Enabling In-Memory Computing for Artificial Intelligence Part 1: The Analog Approach

In this two-part series, we will explore and evaluate digital and analog computing and highlight the...
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A Fresh Take on Neural Network Pruning from the Angle of Graph Theory

Model pruning is arguably one of the oldest methods of deep neural networks (DNN) model size reducti...
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ULTRA: Foundation Models for Knowledge Graph Reasoning

Training a single generic model for solving arbitrary datasets is always a dream for ML researchers,...
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Knowledge Retrieval Takes Center Stage

GenAI Architecture Shifting from RAG Toward Interpretive Retrieval-Centric Generation (RCG) Models
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Accelerating Graph Neural Network Training on Intel CPU through Fused Sampling & Hybrid Partitioning

Intel Labs and AIA developed a new graph sampling method called “fused sampling” that achieves up to...
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Intel Labs Researcher Spotlight: James Jaussi and Integrated Photonics

James Jaussi, Senior PE and Director of the PHY Research Lab at Intel Labs, and his team perform int...
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Multi-Objective GFlowNet: Intel Labs, Mila and Recursion Collaborate on AI for Scientific Discovery

Intel Labs, Mila, and Recursion Pharmaceuticals collaborated on Multi-Objective GFlowNets (MOGFNs), ...
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Intel Labs Releases Open MatSci ML Toolkit 1.0 for Training AI Models on Materials Science

Intel Labs recently released the Open MatSci ML Toolkit version 1.0 on August 31, making training of...
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Intel Presents Latest Computer Vision Research at ICCV 2023

Intel presents six computer vision works at he 2023 International Conference on Computer Vision (ICC...
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Intel Labs Presents Quantum Circuit Optimizations at IEEE Quantum Week

This year’s IEEE International Conference on Quantum Computing and Engineering (QCE), or Quantum Wee...
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Accelerating Codegen training and inference on Habana Gaudi2

Optimum Habana makes it easy to achieve fast training and inference of large language models (LLMs)...
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FAENet: Intel Labs and Mila Collaborate on Data-Centric AI Model for Materials Property Modeling

Intel and Mila collaborated on FAENet, a new data-centric model paradigm that improves both modeling...
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Intel and Collaborators Present Latest Database Research at VLDB 2023

Intel presents eight co-authored contributions across the research, industrial, and demonstrations t...
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MatSci-NLP: Intel Labs and Mila Collaborate on Benchmark to Assess Materials Science Language Models

Intel and Mila collaborate on MatSci-NLP, the first broad benchmark for assessing the capabilities o...
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Intel Labs Selected by DARPA H6 to Develop Tactical-Grade Clock with Microsecond Timing Precision

Intel Labs and collaborators from the University of Pennsylvania, Carnegie Mellon University, and IS...
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ProtST: Intel and Mila Collaborate on a New Multi-Modal Protein Language Model

Intel and Mila collaborated on ProtST, a multi-modal protein language model that enables users to cr...
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Survival of the Fittest: Compact Generative AI Models Are the Future for Cost-Effective AI at Scale

The case for nimble, targeted, retrieval-based models as the best solution for generative AI applica...
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Intel Presents Six Papers on Novel AI Research at ICML 2023

Intel had six papers accepted at the 40th International Conference on Machine Learning (ICML) 2023, ...
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Intel Xeon is all you need for AI inference: Performance Leadership on Real World Applications

Intel is democratizing AI inference by delivering a better price and performance forreal-world use c...
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Intel® Xeon® trains Graph Neural Network models in record time

The 4th gen Intel® Xeon® Scalable Processor, formerly codenamed Sapphire Rapids is a balanced platfo...
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Intel Labs Research on Intent-Driven Orchestration Leads to Simplifying Cloud and Edge Deployments

Using research from Intel Labs, Intel has introduced the open-source Intent-Driven Orchestration (ID...
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JUMP and Intel Labs Success Story: UCSD Professor Leads HD Computing Research Efforts

For the past five years at the JUMP CRISP Center at UCSD, Professor Tajana Simunic Rosing has led hy...
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Can we Improve Early-Exit Transformers? Novel Adaptive Inference Method Presented at ACL 2023

Intel Labs and the Hebrew University of Jerusalem present SWEET, an adaptive Inference method for te...
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Intel’s Leading Contributions at SIGMOD’s Data Management on New Hardware (DaMoN) Workshop

The 19th International Workshop on Data Management on New Hardware (DaMoN) was co-located with the 2...
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