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Found 23 papers, 7 papers with code
A DEEP ADVERSARIAL LEARNING METHODOLOGY FOR DESIGNING MICROSTRUCTURAL MATERIAL SYSTEMS
Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics.
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
We use the problem of learning properties of inorganic materials from numerical attributes derived from material composition and/or crystal structure to compare IRNet's performance against that of other machine learning techniques.
Characterizing Attacks on Deep Reinforcement Learning
To the best of our knowledge, we are the first to apply adversarial attacks on DRL systems to physical robots.
A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes
As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run.
Scalable Attack on Graph Data by Injecting Vicious Nodes
Recent studies have shown that graph convolution networks (GCNs) are vulnerable to carefully designed attacks, which aim to cause misclassification of a specific node on the graph with unnoticeable perturbations.
An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety
In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.
GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts
Program synthesis is the task to automatically generate programs based on user specification.
Theory and Applications of Financial Chaos Index
We develop a new stock market index that captures the chaos existing in the market by measuring the mutual changes of asset prices.
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
Microstructural materials design is one of the most important applications of inverse modeling in materials science.
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL
However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries.
An Overview & Analysis of Sequence-to-Sequence Emotional Voice Conversion
Emotional voice conversion (EVC) focuses on converting a speech utterance from a source to a target emotion; it can thus be a key enabling technology for human-computer interaction applications and beyond.
A Temporal-oriented Broadcast ResNet for COVID-19 Detection
Detecting COVID-19 from audio signals, such as breathing and coughing, can be used as a fast and efficient pre-testing method to reduce the virus transmission.
Exploring speaker enrolment for few-shot personalisation in emotional vocalisation prediction
In this work, we explore a novel few-shot personalisation architecture for emotional vocalisation prediction.
Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression
We propose a novel Dynamic Restrained Uncertainty Weighting Loss to experimentally handle the problem of balancing the contributions of multiple tasks on the ICML ExVo 2022 Challenge.
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human-computer interaction research.
An Overview about Emerging Technologies of Autonomous Driving
Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications.
MM-NeRF: Multimodal-Guided 3D Multi-Style Transfer of Neural Radiance Field
3D style transfer aims to generate stylized views of 3D scenes with specified styles, which requires high-quality generating and keeping multi-view consistency.
RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence
Although federated learning (FL) is a promising tool to solve the problem, adapting conventional FL frameworks to VCI is nontrivial.
Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation
An optimized scenario generation toolkit is developed within BridgeGen.
Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction
In this paper, we propose a numerical reasoning method for material KGs (NR-KG), which constructs a cross-modal KG using semantic nodes and numerical proxy nodes.
LitSim: Conflict-aware Policy for Long-term Interactive Traffic Simulation
Simulation is pivotal in evaluating the performance of autonomous driving systems due to the advantages in efficiency and cost compared to on-road testing.
Virtual Foundry Graphnet for Metal Sintering Deformation Prediction
Metal Sintering is a necessary step for Metal Injection Molded parts and binder jet such as HP's metal 3D printer.
