Category: Machine Learning & Data Science

AI Machine Learning & Data Science Research

Waymo & Google’s PolyLoss: Tailoring Loss Functions to Different Tasks and Datasets

Waymo and Google researchers’ new paper A Polynomial Expansion Perspective of Classification Loss Functions presents PolyLoss, a novel and simple framework that redesigns loss functions as a linear combination of polynomial functions that can be tailored to different target tasks and datasets.

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.

AI Machine Learning & Data Science Research

UTokyo’s Novel Self-Blended Images Approach Achieves SOTA Results in Deepfake Detection

A research team from the University of Tokyo addresses the challenge of deepfake detection in their new paper Detecting Deepfakes with Self-Blended Images, proposing self-blended images (SBIs), a novel synthetic training data approach that outperforms state-of-the-art methods on unseen manipulations and scenes for deepfake detection tasks.

AI Machine Learning & Data Science Research

DeepMind, Mila & Google Brain Enable Generalization Capabilities for Causal Graph Structure Induction

A research team from DeepMind, Mila – University of Montreal and Google Brain proposes a neural network architecture that learns the graph structure of observational and/or interventional data via supervised training on synthetic graphs, making causal induction a black-box problem that generalizes well to new synthetic and naturalistic graphs.

AI Computer Vision & Graphics Machine Learning & Data Science Research

UC Berkeley & Intel’s Photorealistic Denoising Method Boosts Video Quality on Moonless Nights

In the new paper Dancing Under the Stars: Video Denoising in Starlight, a research team from UC Berkeley and Intel Labs leverages a GAN-tuned, physics-based noise model to represent camera noise under low light conditions and trains a novel denoiser that, for the first time, achieves photorealistic video denoising in starlight.

AI Machine Learning & Data Science Popular Research

Toward Self-Improving Neural Networks: Schmidhuber Team’s Scalable Self-Referential Weight Matrix Learns to Modify Itself

In the new paper A Modern Self-Referential Weight Matrix That Learns to Modify Itself, a research team from The Swiss AI Lab, IDSIA, University of Lugano (USI) & SUPSI, and King Abdullah University of Science and Technology (KAUST) presents a scalable self-referential weight matrix (SRWM) that leverages outer products and the delta update rule to update and improve itself.

AI Machine Learning & Data Science Research

Alibaba’s USI: A Unified Scheme for Training Any Backbone on ImageNet That Delivers Top Results Without Hyperparameter Tuning

In the new paper Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results, a research team from Alibaba Group’s DAMO Academy introduces USI (Unified Scheme for ImageNet), a unified scheme for training any backbone on ImageNet that does not require adjustments or hyperparameter tuning between different models, and consistently yields top model results in terms of accuracy and efficiency.

AI Machine Learning & Data Science Research

OpenAI’s unCLIP Text-to-Image System Leverages Contrastive and Diffusion Models to Achieve SOTA Performance

In the new paper Hierarchical Text-Conditional Image Generation with CLIP Latents, an OpenAI research team combines the advantages of contrastive and diffusion models for text-conditional image generation tasks. Their proposed unCLIP model improves image diversity with minimal loss in photorealism and caption similarity, and produces image quality comparable to the state-of-the-art text-to-image system GLIDE.

AI Machine Learning & Data Science Research

Google Builds Language Models with Socratic Dialogue to Improve Zero-Shot Multimodal Reasoning Capabilities

In the new paper Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language, Google researchers argue that the diversity of different foundation models is symbiotic and that it is possible to build a framework that uses structured Socratic dialogue between pre-existing foundation models to formulate new multimodal tasks as a guided exchange between the models without additional finetuning.

AI Machine Learning & Data Science Research

EPFL’s Multi-modal Multi-task Masked Autoencoder: A Simple, Flexible and Effective ViT Pretraining Strategy Applicable to Any RGB Dataset

The Swiss Federal Institute of Technology Lausanne (EPFL) presents Multi-modal Multi-task Masked Autoencoders (MultiMAE), a simple and effective pretraining strategy that enables masked autoencoding to include multiple modalities and tasks and is applicable to any RGB dataset.

AI Machine Learning & Data Science Nature Language Tech Research

Training Compute-Optimal Large Language Models: DeepMind’s 70B Parameter Chinchilla Outperforms 530B Parameter Megatron-Turing

In the new paper Training Compute-Optimal Large Language Models, a DeepMind research team posits that current large language models are significantly undertrained and, based on empirical outcomes of over 400 training runs, proposes three predictive approaches for optimally setting model size and training duration.

AI Machine Learning & Data Science Research

CMU & Google Extend Pretrained Models to Thousands of Underrepresented Languages Without Using Monolingual Data

A research team from Carnegie Mellon University and Google systematically explores strategies for leveraging the relatively under-studied resource of bilingual lexicons to adapt pretrained multilingual models to low-resource languages. Their resulting Lexicon-based Adaptation approach produces consistent performance improvements without requiring additional monolingual text.

AI Machine Learning & Data Science Nature Language Tech Research

Google, NYU & Maryland U’s Token-Dropping Approach Reduces BERT Pretraining Time by 25%

In the new paper Token Dropping for Efficient BERT Pretraining, a research team from Google, New York University, and the University of Maryland proposes a simple but effective “token dropping” technique that significantly reduces the pretraining cost of transformer models such as BERT without hurting performance on downstream fine-tuning tasks.

AI Machine Learning & Data Science Popular Research

DeepMind Proposes Symmetry-Based Representations as a Fundamental Principle for Learning Good Representations in General Intelligence

A DeepMind research team argues that the mathematical description of symmetries in group theory is an important foundation that determines the structure of the universe, constrains the nature of natural tasks, and consequently shapes both biological and artificial intelligence. The study proposes symmetry transformations as a fundamental principle for defining what makes good representations.

AI Machine Learning & Data Science Research

Google Extends Transformers for Immediate Knowledge Acquisition via a Simple New Data Read & Memorize Technique

A Google research team addresses conventional transformers’ resource-heavy training and fine-tuning requirements for learning new knowledge, proposing Memorizing Transformers as a step toward language models that can simply read and memorize new data at inference time for immediate knowledge acquisition.

AI Machine Learning & Data Science Nature Language Tech Research

Google & IDSIA’s Block-Recurrent Transformer Dramatically Outperforms Transformers Over Very Long Sequences

A team from Google Research and the Swiss AI Lab IDSIA proposes the Block-Recurrent Transformer, a novel long-sequence processing approach that has the same computation time and parameter count costs as a conventional transformer layer but achieves significant perplexity improvements in language modelling tasks over very long sequences.

AI Machine Learning & Data Science Research

Ithaca Paper Published in Nature: The First DNN Designed for Textual Restoration and Geographical and Chronological Attribution of Ancient Greek Inscriptions

A research team from DeepMind, Ca’ Foscari University of Venice, University of Oxford and Athens University of Economics and Business introduces Ithaca, a deep neural network (DNN) designed for textual restoration and geographical and chronological attribution of ancient Greek inscriptions.

AI Machine Learning & Data Science Research

Microsoft & OpenAI’s µTransfer Zero-Shot Hyperparameter Transfer Method Tunes GPT-3’s Hyperparameters on a Single GPU

In the new paper Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer, Microsoft and OpenAI researchers propose µTransfer, a method that leverages Maximal Update Parametrization (µP) to zero-shot transfer hyperparameters from small models and obtain near-optimal parameters on large models without directly tuning them.

AI Machine Learning & Data Science Research

OpenAI’s AutoDIME: Automating Multi-Agent Environment Design for RL Agents

In the new paper AutoDIME: Automatic Design of Interesting Multi-Agent Environments, an OpenAI research team explores automatic environment design for multi-agent environments using an RL-trained teacher that samples environments to maximize student learning. The work demonstrates that intrinsic teacher rewards are a promising approach for automating both single and multi-agent environment design.

AI Machine Learning & Data Science Research

DeepMind Trains AI Agents Capable of Robust Real-time Cultural Transmission Without Human Data

In the new paper Learning Robust Real-Time Cultural Transmission Without Human Data, a DeepMind research team proposes a procedure for training artificially intelligent agents capable of flexible, high-recall, robust real-time cultural transmission from human co-players in a rich 3D physical simulation without using human data in the training pipeline.

AI Machine Learning & Data Science Research

Meet TQP: The First Query Processor to Run On Tensor Computation Runtimes Delivers up to 20x Speedups Over CPU-Only Systems

A research team from the University of Washington, UC San Diego and Microsoft prototypes Tensor Query Processor (TQP), a query processor that runs atop tensor computation runtimes (TCRs) such as PyTorch, TVM, and ONNX Runtime, improving query execution time by up to 20x over CPU-only systems and up to 5x over specialized GPU solutions.