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Found 38 papers, 9 papers with code
MCS: An In-battle Commentary System for MOBA Games
This paper introduces a generative system for in-battle real-time commentary in mobile MOBA games.
DeepSeq: Deep Sequential Circuit Learning
Circuit representation learning is a promising research direction in the electronic design automation (EDA) field.
TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training
In this paper, we propose TA-MoE, a topology-aware routing strategy for large-scale MoE trainging, from a model-system co-design perspective, which can dynamically adjust the MoE dispatch pattern according to the network topology.
Peak-First CTC: Reducing the Peak Latency of CTC Models by Applying Peak-First Regularization
To reduce the peak latency, we propose a simple and novel method named peak-first regularization, which utilizes a frame-wise knowledge distillation function to force the probability distribution of the CTC model to shift left along the time axis instead of directly modifying the calculation process of CTC loss and gradients.
Coded Residual Transform for Generalizable Deep Metric Learning
To address this challenge, in this paper, we introduce a new method called coded residual transform (CRT) for deep metric learning to significantly improve its generalization capability.
Fast geometric trim fitting using partial incremental sorting and accumulation
We apply our method to two distinct camera resectioning algorithms, and demonstrate highly efficient and reliable, geometric trim fitting.
SATformer: Transformers for SAT Solving
In this paper, we propose SATformer, a novel Transformer-based solution for Boolean satisfiability (SAT) solving.
TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning
In conclusion, TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines.
PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation
The rational design of novel molecules with desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets.
DeepTPI: Test Point Insertion with Deep Reinforcement Learning
Unlike previous learning-based solutions that formulate the TPI task as a supervised-learning problem, we train a novel DRL agent, instantiated as the combination of a graph neural network (GNN) and a Deep Q-Learning network (DQN), to maximize the test coverage improvement.
DeepSAT: An EDA-Driven Learning Framework for SAT
Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling.
RAR-PINN algorithm for the data-driven vector-soliton solutions and parameter discovery of coupled nonlinear equations
This work aims to provide an effective deep learning framework to predict the vector-soliton solutions of the coupled nonlinear equations and their interactions.
FKreg: A MATLAB toolbox for fast Multivariate Kernel Regression
Kernel smooth is the most fundamental technique for data density and regression estimation.
DeepGate: Learning Neural Representations of Logic Gates
We propose DeepGate, a novel representation learning solution that effectively embeds both logic function and structural information of a circuit as vectors on each gate.
Testability-Aware Low Power Controller Design with Evolutionary Learning
The proposed GA-guided XORNets also allows reducing the number of control bits, and the total testing time decreases by 20. 78% on average and up to 47. 09% compared to the existing design without sacrificing test coverage.
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
In this work, we propose a novel tree-structured wavelet neural network for time series signal analysis, namely T-WaveNet, by taking advantage of an inherent property of various types of signals, known as the dominant frequency range.
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition
Graph convolutional networks (GCNs) have emerged as dominant methods for skeleton-based action recognition.
Rethinking the Misalignment Problem in Dense Object Detection
On the basis of SALT and SDR loss, we propose SALT-Net, which explicitly exploits task-aligned point-set features for accurate detection results.
A Pseudo Label-wise Attention Network for Automatic ICD Coding
The label-wise attention mechanism is widely used in automatic ICD coding because it can assign weights to every word in full Electronic Medical Records (EMR) for different ICD codes.
Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification
In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes's group using a multi-objective optimization genetic algorithm.
RFCBF: enhance the performance and stability of Fast Correlation-Based Filter
Feature selection is a preprocessing step which plays a crucial role in the domain of machine learning and data mining.
TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks
To be specific, we first build a similarity graph on test instances and training samples, and we conduct graph-based semi-supervised learning to extract contextual features.
AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference
This paper presents AppealNet, a novel edge/cloud collaborative architecture that runs deep learning (DL) tasks more efficiently than state-of-the-art solutions.
Objects as Extreme Points
We also propose a new metric to measure the similarity between two groups of extreme points, namely, Extreme Intersection over Union (EIoU), and incorporate this EIoU as a new regression loss.
Skimming and Scanning for Untrimmed Video Action Recognition
Video action recognition (VAR) is a primary task of video understanding, and untrimmed videos are more common in real-life scenes.
BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions
Therefore, we hope to develop a novel supervised learning method to learn the PPAs and DDAs effectively and thereby improve the prediction performance of the specific task of DPI.
KddRES: A Multi-level Knowledge-driven Dialogue Dataset for Restaurant Towards Customized Dialogue System
In this paper, we publish a first Cantonese knowledge-driven Dialogue Dataset for REStaurant (KddRES) in Hong Kong, which grounds the information in multi-turn conversations to one specific restaurant.
a-Tucker: Input-Adaptive and Matricization-Free Tucker Decomposition for Dense Tensors on CPUs and GPUs
A mode-wise flexible Tucker decomposition algorithm is proposed to enable the switch of different solvers for the factor matrices and core tensor, and a machine-learning adaptive solver selector is applied to automatically cope with the variations of both the input data and the hardware.
DeepDyve: Dynamic Verification for Deep Neural Networks
The key to enabling such lightweight checking is that the smaller neural network only needs to produce approximate results for the initial task without sacrificing fault coverage much.
Robust Adaptive Beam Tracking for Mobile Millimetre Wave Communications
Millimetre wave (mmWave) beam tracking is a challenging task because tracking algorithms are required to provide consistent high accuracy with low probability of loss of track and minimal overhead.
Efficient Alternating Least Squares Algorithms for Low Multilinear Rank Approximation of Tensors
In this paper, we propose a new class of truncated HOSVD algorithms based on alternating least squares (ALS) for efficiently computing the low multilinear rank approximation of tensors.
Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials
From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera.
UAV-Enabled Confidential Data Collection in Wireless Networks
This work, for the first time, considers confidential data collection in the context of unmanned aerial vehicle (UAV) wireless networks, where the scheduled ground sensor node (SN) intends to transmit confidential information to the UAV without being intercepted by other unscheduled ground SNs.
On Configurable Defense against Adversarial Example Attacks
Machine learning systems based on deep neural networks (DNNs) have gained mainstream adoption in many applications.
DIMSIM: An Accurate Chinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding
Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks.
Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing
The alternating direction method of multipliers (ADMM) is an important tool for solving complex optimization problems, but it involves minimization sub-steps that are often difficult to solve efficiently.
Identifying the Absorption Bump with Deep Learning
In this paper, we apply Deep Learning techniques to detect the broad absorption bump.
Adaptive Primal-Dual Hybrid Gradient Methods for Saddle-Point Problems
The Primal-Dual hybrid gradient (PDHG) method is a powerful optimization scheme that breaks complex problems into simple sub-steps.
Numerical Analysis 65K15 G.1.6
