With the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem. In this paper, we propose DeepFreight, a model-free deep-reinforceme...
The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically signific...
An overview is given about the capabilities provided by the JLab 12 GeV Upgrade to measure deeply virtual exclusive processes with high statistics and covering a large kinematics range in the parameters that are needed to allow reconstruction of a...
This paper describes our deep learning-based approach to multilingual aspect-based sentiment analysis as part of SemEval 2016 Task 5. We use a convolutional neural network (CNN) for both aspect extraction and aspect-based sentiment analysis. We cast...
Multi-agent reinforcement learning focuses on training the behaviors of multiple learning agents that coexist in a shared environment. Recently, MARL models, such as the Multi-Agent Transformer (MAT) and ACtion dEpendent deep Q-learning (ACE), have s...
This paper presents a novel deep learning model based on the transformer architecture to predict the load-deformation behavior of large bored piles in Bangkok subsoil. The model encodes the soil profile and pile features as tokenization input, and ge...
We describe deep, new, wide-field radio continuum observations of the Great Observatories Origins Deep Survey -- North (GOODS-N) field. The resulting map has a synthesized beamsize of ~1.7" and an r.m.s. noise level of ~3.9uJy/bm near its center and...
This article appears as chapter 21 of Prince (2023, Understanding Deep Learning); a complete draft of the textbook is available here: http://udlbook.com. This chapter considers potential harms arising from the design and use of AI systems. These incl...
DeepRobust is a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field. It currently contains more than 10 attack algorithms and 8 defense algorithms in image domain and 9 attac...
Recent advancements in deep-learning methods for object detection in point-cloud data have enabled numerous roadside applications, fostering improvements in transportation safety and management. However, the intricate nature of point-cloud data poses...
This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used...
We present the first three-fold differential measurement for neutral pion multiplicity ratios produced in semi-inclusive deep-inelastic electron scattering on carbon, iron and lead nuclei normalized to deuterium from CLAS at Jefferson Lab. We found t...
[.](https://i.imgur.com/q7NcbP1.jpg) *LAST UPDATED : Dec 27* [bit.ly/HelpHongKong](https://bit.ly/HelpHongKong) >Our deepest fear is not that we are inadequate. Our deepest fear is that we are po...
On behalf of the survey teams I summarize the designs and results of the Las Campanas Infrared Survey and Gemini Deep Deep Survey, both of which were initiated to understand the nature of red galaxies and to study the history of stellar mass assemb...
We propose a deep representation of appearance, i. e., the relation of color, surface orientation, viewer position, material and illumination. Previous approaches have useddeep learning to extract classic appearance representationsrelating to reflect...
This work focuses on learning useful and robust deep world models using multiple, possibly unreliable, sensors. We find that current methods do not sufficiently encourage a shared representation between modalities; this can cause poor performance on...
In this paper, we present ViP-DeepLab, a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision, which we model as restoring the point clouds from perspective image sequences while providing each poi...
We introduce YOGA, a deep learning based yet lightweight object detection model that can operate on low-end edge devices while still achieving competitive accuracy. The YOGA architecture consists of a two-phase feature learning pipeline with a cheap...
Deep equilibrium models (DEQs) have proven to be very powerful for learning data representations. The idea is to replace traditional (explicit) feedforward neural networks with an implicit fixed-point equation, which allows to decouple the forward an...
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