Month: April 2023

by Synced 2023-04-29 2
AI Machine Learning & Data Science Research

Microsoft & Peking U’s WizardLM Enables LLMs to Automatically Mass-Produce Complex Instructions

In the new paper WizardLM: Empowering Large Language Models to Follow Complex Instructions, a research team from Microsoft and Peking University presents Evol-Instruct, a novel approach that leverages LLMs to automatically generate large amounts of instruction data with varying levels of complexity. In human evaluations, the team’s resulting WizardLM model’s generated instructions were judged superior to human-created instruction datasets.

by Synced 2023-04-27 3
AI Machine Learning & Data Science Research

UC Berkeley’s FastRLAP Learns Aggressive and Effective High-Speed Driving Strategies With <20 Minutes of Real-World

In the new paper FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing, a UC Berkeley research team proposes FastRLAP (Fast Reinforcement Learning via Autonomous Practicing), a system that autonomously practices in the real world and learns aggressive maneuvers to enable effective high-speed driving.

by Synced 2023-04-26 Number of comments0
AI Machine Learning & Data Science Research

Microsoft’s NaturalSpeech 2 Outperforms Previous TTS Systems in Zero-Shot Speech and Singing Synthesis

In the new paper NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers, a Microsoft team introduces NaturalSpeech 2, a TTS system with latent diffusion models for natural and strong zero-shot voice synthesis that captures expressive prosodies with superior robustness.

by Synced 2023-04-24 Number of comments0
AI Computer Vision & Graphics Machine Learning & Data Science Research

Look Again, YOLO: Baidu’s RT-DETR Detection Transformer Achieves SOTA Results on Real-Time Object Detection

In the new paper DETRs Beat YOLOs on Real-Time Object Detection, a Baidu Inc. research team presents Real-Time Detection Transformer (RT-DETR), a real-time end-to-end object detector that leverages a hybrid encoder and novel IoU-aware query selection to address inference speed delay issues. RT-DETR outperforms YOLO object detectors in both accuracy and speed.

by Synced 2023-04-20 Number of comments0
AI Machine Learning & Data Science Research

Huawei’s DiffFit Unlocks the Transferability of Large Diffusion Models to New Domains

In the new paper DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning, a Huawei Noah’s Ark Lab research team introduces DiffFit, a parameter-efficient fine-tuning technique that enables fast adaptation to new domains for diffusion image generation. Compared to full fine-tuning approaches, DiffFit achieves 2x training speed-ups while using only ~0.12 percent of trainable parameters.

by Synced 2023-04-19 3
AI Machine Learning & Data Science Research

DeepMind & MPG Establish a Research Program for Meta-Learned Models of Cognition

In the new paper Meta-Learned Models of Cognition, a team from the Max Planck Institute for Biological Cybernetics (Max-Planck-Gesellschaft, MPG) and DeepMind proposes the establishment of a research program focused on meta-learned models of cognition. The team cites machine learning papers demonstrating how meta-learning can be used to construct Bayes-optimal learning algorithms and suggests it can significantly expand the scope of the rational analysis of cognition.

by Synced 2023-04-18 6
AI Computer Vision & Graphics Machine Learning & Data Science Research

Microsoft & Bath U’s SpectFormer Significantly Improves Vision Transformers via Frequency and Attention

In the new paper SpectFormer: Frequency and Attention Is What You Need in a Vision Transformer, a research team from Microsoft and the University of Bath proposes Spectformer, a novel transformer architecture that combines spectral and multi-headed attention layers to better capture appropriate feature representations and improve performance.

by Synced 2023-04-13 3
AI Machine Learning & Data Science Nature Language Tech Research

Microsoft’s LLMA Accelerates LLM Generations via an ‘Inference-With-Reference’ Decoding Approach

In the new paper Inference with Reference: Lossless Acceleration of Large Language Models, a Microsoft research team proposes LLMA, an inference-with-reference decoding mechanism that achieves up to 2x lossless speed-ups with identical generation results by exploiting the overlaps between LLM outputs and references.

by Synced 2023-04-03 3
AI Machine Learning & Data Science Research

Meet TaskMatrix.AI: A Microsoft ‘Super-AI’ That Links Foundation Models With Millions of APIs to Perform Diverse Tasks

In the new paper TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs, a Microsoft research team proposes TaskMatrix.AI, a novel ecosystem that connects foundation models with millions of existing models and system APIs to build a “super-AI” capable of addressing a wide range of digital and physical tasks.