Computer-aided Systems May Speed up New Anticancer Drug Development
Researchers analyzed different subareas of CADD process with a focus on anticancer drugs.
Tag: Research
Deep Claim: Payer Response Prediction from Claims Data with Deep Learning
The paper outlines a predictive model we’ve developed that has the potential to help significantly reduce wasteful healthcare spending.
Salesforce’s AI-Powered Economist Seeks to Optimize Income Equality and Productivity
The AI Economist uses a two-level RL framework where a collection of AI agents simulate how humans might react to various taxes in a principled economic simulation.
Can AI Reimagine City Configuration and Automate Urban Planning?
Researchers propose reducing the workloads of urban planners by introducing deep learning systems to handle some of their responsibilities.
Novel Algorithm Proposed for Sensory Event Detection via Flexible Film Electrodes
Researchers proposed an optimized deep learning algorithm for detecting sensory events occurring during stimulation of the nerves using a stretchable and flexible electrocorticography (ECoG) electrode.
Intel Lab Transforms Your Phone into a Robot for $50
Intel Labs researchers have proposed a novel method for building a robot called “OpenBot” on just a US$50 budget.
Detecting Deepfakes: MIT CSAIL Model Identifies Manipulations Using Local Artifacts
Researchers proposed a new model that is designed to spot deepfakes by looking at subtle visual artifacts.
Fewer Cameras and Faster Processing: USC Proposes Monocular Real-Time Volumetric Performance Capture
A novel volumetric capture system that is capable of fully capturing clothed human bodies in real-time using only a single RGB webcam.
Sepp Hochreiter on Parallels Between Attention Mechanisms and Modern Hopfield Networks
Researchers argue that “attention mechanism is the update rule of a modern Hopfield network with continuous states.”
New ML Algorithm Tunes Quantum Devices Faster Than Human Experts
The proposed algorithm is approximately 180 times faster than an automated random search of the parameter space.
Using Deep RL to Model Human Locomotion Control in Neuromechanical Simulations
Researchers from Stanford University, UC Berkeley and CMU review neuromechanical simulations and DRL, with a focus on modelling the control of human locomotion.
MRI Study Reveals Arterial Culprit Plaque Characteristics
Researchers developed a head-neck combined HR-MRVWI technique that could potentially provide remarkable suppression of cerebrospinal fluid signals.
Fast and Furious! DRL-Fuelled Agents Grab Pole Position in Gran Turismo Sport
University of Zurich and ETH Zurich’s deep reinforcement learning agents have achieved superhuman performance in the time trials of popular racing simulator game Gran Turismo Sport.
DeepMind & McGill Employ Operations Generalization to Divide & Conquer Complex RL Problems
DeepMind and McGill University researchers believe the problem could be solved through a strategic “divide-and-conquer” approach.
Yoshua Bengio’s Improved Rate-Based Curriculum Learning Outperforms Progress-Based Algorithms
Bengio and researchers from Université de Montréal and the École Normale Supérieure in Paris have introduced a new curriculum learning algorithm based on the notion of mastering rate.
Google Introduces NLP Model Understanding Tool
Google Research released a paper tackling this issue with a new open-source analytic platform: the Language Interpretability Tool (LIT).
Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration
To adapt conventional deep models to real scenarios, a research work carried by a team (XPixel) from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences investigated the use of additional branches to tune imagery effects.
Love-Love: Stanford Researchers Generate Realistic ‘Fake’ Wimbledon
A Stanford University research team has responded with an AI-powered model capable of realistically simulating a Wimbledon final and more.
Introducing Holicity: A City-Scale Dataset for Learning Holistic 3D Structures
HoliCity, a city-scale dataset and all-in-one data platform for research into learning abstracted high-level holistic 3D structures derived from city CAD (computer-aided design) models.
Eyes on Me: Google AI ‘MediaPipe Iris’ Improves Iris Tracking and Distance Estimation
MediaPipe Iris, a novel machine learning model designed to deliver accurate iris estimation without using depth sensors.
Pixel2Style2Pixel: Novel Encoder Architecture Boosts Facial Image-To-Image Translation
Researchers from Penta-AI and Tel-Aviv University introduce a generic image-to-image translation framework dubbed Pixel2Style2Pixel (pSp).
SIAT Researchers Design Novel Deep Learning System to Assess Skeletal Maturity
Researchers introduced an ensemble-based deep learning pipeline to automatically assess the distal radius and ulna (DRU) maturity from left-hand radiographs.
ArXiv’s 1.7M+ Research Papers Now Available on Kaggle
ArXiv announced that all of its research papers are now available on Kaggle.
DeepMind & Google Explore Hyperparameter Selection for Offline RL
Researchers conducted a thorough empirical study of hyperparameter selection for offline RL, aiming to identify and develop more reliable and effective approaches.
Unselfie Turns Arm-Extended Selfies into Neutral-Pose Portraits
“Unselfie,” a novel photographic transformation model that can automatically translate selfies into neutral-pose portraits.
Google ‘BigBird’ Achieves SOTA Performance on Long-Context NLP Tasks
BigBird is shown to dramatically improve performance across long-context NLP tasks, producing SOTA results in question answering and summarization.
Applying Linearly Scalable Transformers to Model Longer Protein Sequences
Researchers proposed a new transformer architecture called “Performer” — based on what they call fast attention via orthogonal random features (FAVOR).
Google Proposes Lasso Algorithm Variant for Learning Convolutions: ‘Bridging the Gap Between Fully-Connected and Convolutional Nets’
Unlike previous work that suggested fully-connected networks can only deliver subpar performance, Neyshabur’s experiments showed a significant performance jump.
Asia China Computer Vision & Graphics Health Machine Learning & Data Science Research Shenzhen Institute of Advanced Technology
About Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
SIAT aims to enhance the innovative capacity of the equipment manufacturing and service industries in the Guangdong-Hong Kong region, promote the development of emerging industries possessing their own proprietary intellectual property, and become a world-class industrial research institute.
SIAT Researchers Propose Novel Correction Method for Gas Sensor Drift Problem
Researchers developed a subspace learning-based domain adaptation method, which could potentially alleviate the gas sensor drift problem.
New White-Box Framework Will Cartoonize Your World
This framework can generate high-quality cartoonized images with much-improved controllability.
Adobe and Stanford Unveil SOTA Method for Human Pose Estimation
The researchers say the approach produces motions that are visually and physically much more plausible than state-of-the-art methods.
DeepMind’s AlignNet Learns Stable Object Representations Across Time
New research from UK based AI company and research lab DeepMind is enabling AI agents to perceive dynamic real-world environments more like humans do.
Microsoft & MIT Apply Adversarially Robust Models for Better Transfer Learning
Despite being less accurate on ImageNet, adversarially robust neural networks can match or improve their standard counterparts’ transfer performance.
Joint Spatial-Temporal Transformation Learning Boosts Video Inpainting Performance
Researchers introduce a joint Spatial-Temporal Transformer Network (STTN) to tackle such video inpainting challenges.
Facebook & Inria Propose High-Performance Self-Supervised Technique for CV Tasks
Researchers have developed a new technique for self-supervised training of convolutional networks used for image classification and other computer vision tasks.
Automating Machine Learning: Google AutoML-Zero Evolves ML Algorithms From Scratch
Google researchers propose an “AutoML-Zero” approach designed to automatically search for machine learning (ML) algorithms from scratch, requiring minimal human expertise or input.
Fujitsu Develops World’s First AI technology to Accurately Capture Characteristics of High-Dimensional Data Without Labeled Training Data
Fujitsu has developed the world’s first AI technology that accurately captures the characteristics of high-dimensional data without labeled training data.
SIAT Researchers Propose Novel Deep Learning Method for Positron Emission Tomography
Researchers developed a novel deep learning method that reconstructs PET images using a cascading back-projection neural network (bpNet).
Google Bridges Domain Gaps in Semantic Segmentation of LiDAR Point Clouds
A team of Google researchers recently proposed a novel “complete and label” domain adaptation approach.