Category: Research

Technical review of the newest machine intelligence research.

AI Machine Learning & Data Science Research

Allen AI & UW Propose Unified-IO: A High-Performance, Task-Agnostic Model for CV, NLP, and Multi-Modal Tasks

In the new paper Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks, a research team from the Allen Institute for AI and the University of Washington introduces UNIFIED-IO, a neural model that achieves strong performance across a wide variety of vision, language, and multi-modal tasks without task- or modality-specific branches or fine-tuning.

AI Machine Learning & Data Science Research

A WaveNet Rival? Stanford U Study Models Raw Audio Waveforms Over Contexts of 500k Samples

In the new paper GoodBye WaveNet — A Language Model for Raw Audio with Context of 1/2 Million Samples, Stanford University researcher Prateek Verma presents a generative auto-regressive architecture that models audio waveforms over contexts greater than 500,000 samples and outperforms state-of-the-art WaveNet baselines.

AI Machine Learning & Data Science Research

DeepMind Boosts RL Agents’ Retrieval Capability to Tens of Millions of Pieces of Information

In the new paper Large-Scale Retrieval for Reinforcement Learning, a DeepMind research team dramatically expands the information accessible to reinforcement learning (RL) agents, enabling them to attend to tens of millions of information pieces, incorporate new information without retraining, and learn decision making in an end-to-end manner.

AI Machine Learning & Data Science Research

Google Leverages Transformers to Vastly Simplify Neural Video Compression With SOTA Results

In the new paper VCT: A Video Compression Transformer, a Google Research team presents an elegantly simple but powerful video compression transformer (VCT) that does not require architectural biases and priors and learns totally from data without any hand-crafting. VCT is easy to implement and outperforms conventional video compression approaches.

AI Machine Learning & Data Science Research

Wav2Vec 2.0 Learns Brain-Like Representations From Just 600 Hours of Unlabeled Speech Data in New Study

In the new paper Toward a Realistic Model of Speech Processing in the Brain with Self-supervised Learning, researchers show that self-supervised architectures such as Wav2Vec 2.0 can learn brain-like representations from as little as 600 hours of unlabelled speech; and can also learn sound-generic and speech- and language-specific representations similar to those of the prefrontal and temporal cortices.

AI Machine Learning & Data Science Research

444 Authors From 132 Institutions Release BIG-bench: A 204-Task ‘Extremely Difficult and Diverse’ Benchmark for Large Language Models

In the new paper Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models, 444 authors from 132 institutions introduce Beyond the Imitation Game (BIG-bench), a large-scale, extremely difficult and diverse benchmark that includes 204 tasks for predicting the potentially transformative effects of large language models.

AI Machine Learning & Data Science Research

Cambridge, Google & Secondmind’s Neural Diffusion Processes Challenge Gaussian Processes for Describing Rich Distributions Over Functions

In the new paper Neural Diffusion Processes, a research team from the University of Cambridge, Secondmind, and Google Research presents Neural Diffusion Processes (NDPs), a novel framework that learns to sample from rich distributions over functions at a lower computational cost than the true Bayesian posterior of a conventional Gaussian process.

AI Machine Learning & Data Science Research

Yoshua Bengio Team’s Large-Scale Analysis Reveals the Benefits of Modularity and Sparsity for DNNs

In the new paper Is a Modular Architecture Enough?, a research team from Mila and the Université de Montréal conducts a rigorous and thorough quantitative assessment of common modular architectures that reveals the benefits of modularity and sparsity for deep neural networks and the sub-optimality of existing end-to-end learned modular systems.

AI Machine Learning & Data Science Research

Microsoft’s XTC Extreme Lightweight Compression Method for Pretrained Transformers Achieves SOTA Results and 50x Smaller Model Sizes

In the new paper Extreme Compression for Pre-trained Transformers Made Simple and Efficient, a Microsoft research team introduces XTC, a simple yet effective extreme compression pipeline for pretrained transformers that can achieve state-of-the-art results while reducing model size by 50x.

AI Machine Learning & Data Science Research

Gem-Miner: Finding Lottery Tickets at Initialization and Bettering All Baselines at 19x Faster Speeds

In the new paper Rare Gems: Finding Lottery Tickets at Initialization, a research team from Carnegie Mellon University, MBZUAI, Petuum, Inc and the University of Wisconsin-Madison proposes GEM-MINER, an algorithm that finds sparse subnetworks at initialization trainable to accuracy that is comparable or better than iterative magnitude pruning (IMP) with warm-up.

AI Machine Learning & Data Science Research

NVIDIA & UW Introduce Factory: A Set of Physics Simulation Methods and Learning Tools for Contact-Rich Robotic Assembly

In the new paper Factory: Fast Contact for Robotic Assembly, a research team from NVIDIA and the University of Washington introduces Factory, a set of physics simulation methods and robot learning tools for simulating contact-rich interactions in assembly with high accuracy, efficiency, and robustness.

AI Computer Vision & Graphics Machine Learning & Data Science Research

Google Brain’s UViM: A Unified Approach for Modelling Diverse Vision Tasks Without Modifications

In the new paper UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes, a Google Brain research team proposes UViM, a unified approach that leverages language modelling and discrete representation learning to enable the modelling of a wide range of computer vision tasks without task-specific modifications.

AI Machine Learning & Data Science Nature Language Tech Research

Google’s Imagen Text-to-Image Diffusion Model With Deep Language Understanding Defeats DALL-E 2

In the new paper Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding, a Google Brain research team presents Imagen, a text-to-image diffusion model that combines deep language understanding and photorealistic image generation capabilities to achieve a new state-of-the-art FID score of 7.27 on the COCO dataset.

AI Machine Learning & Data Science Nature Language Tech Research

Fact Tracing in LMs: MIT & Google Dataset and Benchmark Track Learned Knowledge Back to the Training Data

In the new paper Tracing Knowledge in Language Models Back to the Training Data, a team from MIT CSAIL and Google Research proposes a benchmark for tracing language models’ assertions to the associated training data, aiming to establish a principled ground truth and mitigate high compute demands for large neural language model training.

AI Machine Learning & Data Science Nature Language Tech Research

Tokyo U & Google Brain Train Large Language Models as Zero-Shot Reasoners

In the new paper Large Language Models are Zero-Shot Reasoners, a research team from the University of Tokyo and Google Brain demonstrates that large language models (LLMs) can become good zero-shot reasoners through the addition of a simple prompt — “Let’s think step by step” — that elicits a step-by-step thinking process before each question is answered. Their Zero-shot-CoT model achieves huge performance gains compared to the zero-shot baseline.

AI Machine Learning & Data Science Research

Meta AI Extends MAEs to Video for Self-Supervised Representation Learning With Minimal Domain Knowledge

In the new paper Masked Autoencoders As Spatiotemporal Learners, a Meta AI research team extends masked autoencoders (MAE) to spatiotemporal representation learning for video. The novel approach introduces negligible inductive biases on space-time while achieving strong empirical results compared to vision transformers (ViTs) and outperforms supervised pretraining by large margins.

AI Machine Learning & Data Science Research

DeepMind’s Meta-Learning Sparse Compression Networks Set New SOTA on Diverse Modality Data Compression

In the new paper Meta-Learning Sparse Compression Networks, a DeepMind research team proposes steps for scaling implicit neural representations (INRs). The resulting meta-learning sparse compression networks can represent diverse data modalities such as images, manifolds, signed distance functions, 3D shapes, and scenes, achieving state-of-the-art results on some of them.

AI Machine Learning & Data Science Research

Huawei Rethinks Logical Synthesis, Proposing a Practical RL-based Approach That Achieves High Efficiency

In the new paper Rethinking Reinforcement Learning Based Logic Synthesis, a research team from Huawei Noah’s Ark Lab develops a novel reinforcement learning-based logic synthesis method to automatically recognize critical operators and produce common operator sequences that are generalizable to unseen circuits.

AI Machine Learning & Data Science Research

AI21 Labs’ Augmented Frozen Language Models Challenge Conventional Fine-Tuning Approaches Without Sacrificing Versatility

In the new paper Standing on the Shoulders of Giant Frozen Language Models, AI21 Labs researchers propose three novel methods for learning small neural modules that specialize a frozen language model to different tasks. Their compute-saving approach outperforms conventional frozen model methods and challenges fine-tuning performance without sacrificing model versatility.

AI Computer Vision & Graphics Machine Learning & Data Science Research

Microsoft Azure Introduces i-Code: A General Framework That Enables Flexible Multimodal Representation Learning

In the new paper i-Code: An Integrative and Composable Multimodal Learning Framework, a Microsoft Azure Cognitive Services Research team presents i-Code, a self-supervised pretraining framework that enables the flexible integration of vision, speech, and language modalities and learns their vector representations in a unified manner.

AI Computer Vision & Graphics Machine Learning & Data Science Research

LSTM Is Back! A Deep Implementation of the Decades-old Architecture Challenges ViTs on Long Sequence Modelling

A research team from Rikkyo University and AnyTech Co., Ltd. examines the suitability of different inductive biases for computer vision and proposes Sequencer, an architectural alternative to ViTs that leverages long short-term memory (LSTM) rather than self-attention layers to achieve ViT-competitive performance on long sequence modelling.

AI Machine Learning & Data Science Research

Tsinghua U & BAAI’s CogView2 Achieves SOTA Competitive Text-to-Image Generation With 10x Speedups

In the new paper CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers, Tsinghua University and the Beijing Academy of Artificial Intelligence researchers pretrain a Cross-Modal general Language Model (CogLM) for text and image token prediction and finetune it for fast super-resolution. The resulting CogView2 hierarchical text-to-image system achieves significant speedups while generating images with better quality at comparable resolutions.

AI Machine Learning & Data Science Research

Northeastern U & Microsoft Expand StyleGAN’s Latent Space to Surpass the SOTA on Real Face Semantic Editing

In the new paper Expanding the Latent Space of StyleGAN for Real Face Editing, a research team from Northeastern University and Microsoft presents a novel two-branch method that expands the latent space of StyleGAN to enable identity-preserving and disentangled-attribute editing for real face images. The proposed approach achieves both qualitative and quantitative improvements over state-of-the-art methods.

AI Machine Learning & Data Science Nature Language Tech Research

Adobe’s UDoc Captures Cross-Modal Correlations in a Unified Pretraining Framework to Improve Document Understanding

In the new paper Unified Pretraining Framework for Document Understanding, an Adobe Research and Adobe Document Cloud team presents a unified pretraining framework for document understanding that enables cross-modal connections, relevant information highlighting in both visual and textual modalities, and cross-modal connections. UDoc achieves impressive performance on various downstream tasks.