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Found 24 papers, 9 papers with code
Decision-making with Imaginary Opponent Models
Specifically, the actor maintains a speculated belief of the opponents, which we call the \textit{imaginary opponent models}, to predict opponents' actions using local observations and makes decisions accordingly.
Learning to Search for Job Shop Scheduling via Deep Reinforcement Learning
Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics.
Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings
Inducing semantic representations directly from speech signals is a highly challenging task but has many useful applications in speech mining and spoken language understanding.
Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning
We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections.
Multi-scale Sampling and Aggregation Network For High Dynamic Range Imaging
High dynamic range (HDR) imaging is a fundamental problem in image processing, which aims to generate well-exposed images, even in the presence of varying illumination in the scenes.
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
We theoretically prove that the policy improvement theorem holds for the preference-guided $\epsilon$-greedy policy and experimentally show that the inferred action preference distribution aligns with the landscape of corresponding Q-values.
Applying Feature Underspecified Lexicon Phonological Features in Multilingual Text-to-Speech
This mapping was tested for whether it could lead to the successful generation of native, non-native, and code-switched speech in the two languages.
ByT5 model for massively multilingual grapheme-to-phoneme conversion
In this study, we tackle massively multilingual grapheme-to-phoneme conversion through implementing G2P models based on ByT5.
Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback
Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks, nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance.
Phone-to-audio alignment without text: A Semi-supervised Approach
The task of phone-to-audio alignment has many applications in speech research.
Applying Phonological Features in Multilingual Text-To-Speech
We tested whether this mapping could lead to the successful generation of native, non-native, and code-switched speech in the two languages.
Synchronising speech segments with musical beats in Mandarin and English singing
Generating synthesised singing voice with models trained on speech data has many advantages due to the models' flexibility and controllability.
DeepFake-o-meter: An Open Platform for DeepFake Detection
In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes.
Comparing acoustic analyses of speech data collected remotely
Face-to-face speech data collection has been next to impossible globally due to COVID-19 restrictions.
Relating spin-foam to canonical loop quantum gravity by graphical calculus
On the other hand, the action of a Euclidean Hamiltonian constraint operator on certain spin network states is calculated by graphical method.
General Relativity and Quantum Cosmology
VSEGAN: Visual Speech Enhancement Generative Adversarial Network
This paper proposes a novel frameworkthat involves visual information for speech enhancement, by in-corporating a Generative Adversarial Network (GAN).
Discovering Clinically Meaningful Shape Features for the Analysis of Tumor Pathology Images
With the advanced imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis.
Electric Vehicle Charging Infrastructure Planning: A Scalable Computational Framework
The optimal charging infrastructure planning problem over a large geospatial area is challenging due to the increasing network sizes of the transportation system and the electric grid.
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).
DSP: A Differential Spatial Prediction Scheme for Comprehensive real industrial datasets
The improved deep reinforcement learning network is then used to search for and learn the hyperparameters of each sample point in the inverse distance weighted model.
Factors in Finetuning Deep Model for Object Detection With Long-Tail Distribution
Our analysis and empirical results show that classes with more samples have higher impact on the feature learning.
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Temporal and contextual information of videos are not fully investigated and utilized.
Factors in Finetuning Deep Model for object detection
Our analysis and empirical results show that classes with more samples have higher impact on the feature learning.
Cross-Scene Crowd Counting via Deep Convolutional Neural Networks
To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count.
Ranked #15 on
Crowd Counting
on WorldExpo’10
