Search By Subject

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement ...

With the availability of massive labeled training data, powerful machine learning models can be trained. However, the traditional I.I.D. assumption that the training and testing data should follow the same distribution is often violated in reality. ...

Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase experiences. Although this problem shares some common issues with the ...

Mobile cloud offloading is indispensable for inference tasks based on large-scale deep models. However, transmitting privacy-rich inference data to the cloud incurs concerns. This paper presents the design of a system called PriMask, in which the mobile ...

Falls are one of the leading causes of death in the elderly people aged 65 and above. In order to prevent death by sending prompt fall detection alarms, non-invasive radio-frequency (RF) based fall detection has attracted significant attention, due to ...

Indoor self-localization is a highly demanded system function for smartphones. The current solutions based on inertial, radio frequency, and geomagnetic sensing may have degraded performance when their limiting factors take effect. In this paper, we ...

Continuous learning (CL) has recently been adopted into edge video analytics, gaining huge success in maintaining high accuracy without constantly retraining DNN models by human intervention. Though existing solutions offer optimized processing ...

Automatically fixing compilation errors can greatly raise the productivity of software development, by guiding the novice or AI programmers to write and debug code. Recently, learning-based program repair has gained extensive attention and became the ...

In this work, we discuss a method to incorporate domain knowledge into a Reinforcement Learning (RL) environment through the process of behavioral cloning, in the context of a district energy management system. Prior knowledge, encoded into heuristic ...

State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. They are prone to overfitting and poor generalization when given limited ...

Generating high-quality and diverse human images is an important yet challenging task in vision and graphics. However, existing generative models often fall short under the high diversity of clothing shapes and textures. Furthermore, the generation ...

Pre-trained language model (LM) has led to significant performance gains in various natural language processing (NLP) applications due to its strong literacy, e.g., the ability to capture word dependencies. However, the existing pre-trained LMs largely ...

Sampling-based approaches in Reinforcement Learning (RL) typically involve learning or maintaining distributions. While many elegant algorithms were proposed in literature, most methods involve prior assumptions of the underlying distributions (eg. ...

Recently, there is growing attention on applying deep reinforcement learning (DRL) to solve the 3D bin packing problem (3D BPP), given its favorable generalization and independence of ground-truth label. However, due to the relatively less informative ...

Studying adversarial attacks on Reinforcement Learning (RL) agents has become a key aspect of developing robust, RL-based solutions. Test-time attacks, which target the post-learning performance of an RL agent's policy, have been well studied in both ...

Cross-Entropy Method (CEM) is a gradient-free direct policy search method, which has greater stability and is insensitive to hyper-parameter tuning. CEM bears similarity to population-based evolutionary methods, but, rather than using a population it ...

Watermarking has become a popular and attractive technique to protect the Intellectual Property (IP) of Deep Learning (DL) models. However, very few studies explore the possibility of watermarking Deep Reinforcement Learning (DRL) models. Common ...

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked, if they have common favorite videos in one view, ...

A single gene can encode for different protein versions through a process called alternative splicing. Since proteins play major roles in cellular functions, aberrant splicing profiles can result in a variety of diseases, including cancers. Alternative ...