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Found 44 papers, 8 papers with code
Towards Agile Robots: Intuitive Robot Position Speculation with Neural Networks
The RPSN incorporates a differentiable inverse kinematic algorithm and a neural network.
Joint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing Networks
By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained.
Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model
Biased enhanced sampling methods utilizing collective variables (CVs) are powerful tools for sampling conformational ensembles.
Cross Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction
Deep neural networks (DNNs) that incorporated lifelong sequential modeling (LSM) have brought great success to recommendation systems in various social media platforms.
Temporal-Aware Refinement for Video-based Human Pose and Shape Recovery
Though significant progress in human pose and shape recovery from monocular RGB images has been made in recent years, obtaining 3D human motion with high accuracy and temporal consistency from videos remains challenging.
Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems.
Backdiff: a diffusion model for generalized transferable protein backmapping
Despite recent progress in data-driven backmapping approaches, devising a backmapping method that can be universally applied across various CG models and proteins remains unresolved.
Online Resource Allocation for Semantic-Aware Edge Computing Systems
To solve this problem, an online low-complexity algorithm is proposed.
Resource Allocation for Semantic-Aware Mobile Edge Computing Systems
Moreover, the closed-form optimal solution of the semantic extraction factor is derived.
Advancing Ad Auction Realism: Practical Insights & Modeling Implications
Contemporary real-world online ad auctions differ from canonical models [Edelman et al., 2007; Varian, 2009] in at least four ways: (1) values and click-through rates can depend upon users' search queries, but advertisers can only partially "tune" their bids to specific queries; (2) advertisers do not know the number, identity, and precise value distribution of competing bidders; (3) advertisers only receive partial, aggregated feedback, and (4) payment rules are only partially known to bidders.
Joint Batching and Scheduling for High-Throughput Multiuser Edge AI with Asynchronous Task Arrivals
In this paper, we study joint batching and (task) scheduling to maximise the throughput (i. e., the number of completed tasks) under the practical assumptions of heterogeneous task arrivals and deadlines.
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data
The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications.
VideoXum: Cross-modal Visual and Textural Summarization of Videos
We propose a new joint video and text summarization task.
Ranked #1 on
Video Summarization
on videoxum
The RoyalFlush System for the WMT 2022 Efficiency Task
This paper describes the submission of the RoyalFlush neural machine translation system for the WMT 2022 translation efficiency task.
Channel Tracking for RIS-aided mmWave Communications Under High Mobility Scenarios
The emerging reconfigurable intelligent surface (RIS) technology is promising for applications in the millimeter wave (mmWave) communication systems to effectively compensate for propagation loss or tackle the blockage issue.
Hybrid-Regressive Neural Machine Translation
HRT achieves the state-of-the-art BLEU score of 28. 49 on the WMT En-De task and is at least 1. 5x faster than AT, regardless of batch size and device.
Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation
Generally, kNN-MT borrows the off-the-shelf context representation in the translation task, e. g., the output of the last decoder layer, as the query vector of the retrieval task.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
Field-wise Embedding Size Search via Structural Hard Auxiliary Mask Pruning for Click-Through Rate Prediction
However, most existing NAS-based works suffer from expensive computational costs, the curse of dimensionality of the search space, and the discrepancy between continuous search space and discrete candidate space.
Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations
Despite routinely circumventing this issue using manifold learning to estimate CVs directly from standard simulations, such methods cannot provide mappings to a low-dimensional manifold from enhanced sampling simulations as the geometry and density of the learned manifold are biased.
Learning Symbolic Operators: A Neurosymbolic Solution for Autonomous Disassembly of Electric Vehicle Battery
The booming of electric vehicles demands efficient battery disassembly for recycling to be environment-friendly.
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU
Our experiments show that previous scaling rules fail in the training of CTR prediction neural networks.
A Novel 3D Non-Stationary Channel Model for 6G Indoor Visible Light Communication Systems
The visible light communication (VLC) technology has attracted much attention in the research of the sixth generation (6G) communication systems.
Colonoscopy polyp detection with massive endoscopic images
We improved an existing end-to-end polyp detection model with better average precision validated by different data sets with trivial cost on detection speed.
Online Time Series Anomaly Detection with State Space Gaussian Processes
We propose r-ssGPFA, an unsupervised online anomaly detection model for uni- and multivariate time series building on the efficient state space formulation of Gaussian processes.
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers
Inspired by gradient-based neural architecture search (NAS) and network pruning methods, people have tackled the NFS problem with Gating approach that inserts a set of differentiable binary gates to drop less informative features.
Optimization of RIS Configurations for Multiple-RIS-Aided mmWave Positioning Systems based on CRLB Analysis
Reconfigurable intelligent surface (RIS) is a promising technology for future millimeter-wave (mmWave) communication systems.
FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images
Radiotherapy is a treatment where radiation is used to eliminate cancer cells.
Infant Cry Classification with Graph Convolutional Networks
We propose an approach of graph convolutional networks for robust infant cry classification.
Optimal Resource Allocation for Multi-UAV Assisted Visible Light Communication
To meet the traffic and illumination demands of the ground users while minimizing the energy consumption of the UAVs, one must optimize UAV deployment, phase shift of RISs, user association and RIS association.
Information Theory Information Theory
Reconfigurable Intelligent Surfaces for 6G Systems: Principles, Applications, and Research Directions
Reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs), are regarded as one of the most promising and revolutionizing techniques for enhancing the spectrum and/or energy efficiency of wireless systems.
Scalable Graph Neural Networks via Bidirectional Propagation
Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1. 8 billion edges in less than half an hour on a single machine.
WeChat Neural Machine Translation Systems for WMT20
We participate in the WMT 2020 shared news translation task on Chinese to English.
Improve black-box sequential anomaly detector relevancy with limited user feedback
Anomaly detectors are often designed to catch statistical anomalies.
SBAT: Video Captioning with Sparse Boundary-Aware Transformer
However, video captioning is a multimodal learning problem, and the video features have much redundancy between different time steps.
Simple and Deep Graph Convolutional Networks
We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}.
Cooperative Rate-Splitting for Secrecy Sum-Rate Enhancement in Multi-antenna Broadcast Channels
In this paper, we employ Cooperative Rate-Splitting (CRS) technique to enhance the Secrecy Sum Rate (SSR) for the Multiple Input Single Output (MISO) Broadcast Channel (BC), consisting of two legitimate users and one eavesdropper, with perfect Channel State Information (CSI) available at all nodes.
Reflections in the Sky: Joint Trajectory and Passive Beamforming Design for Secure UAV Networks with Reconfigurable Intelligent Surface
This paper investigates the problem of secure energy efficiency maximization for a reconfigurable intelligent surface (RIS) assisted uplink wireless communication system, where an unmanned aerial vehicle (UAV) equipped with an RIS works as a mobile relay between the base station (BS) and a group of users.
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e. g. user/item identifiers) and meaningfully transform them in the low-dimensional space.
Fine-tune BERT with Sparse Self-Attention Mechanism
In this paper, we develop a novel Sparse Self-Attention Fine-tuning model (referred as SSAF) which integrates sparsity into self-attention mechanism to enhance the fine-tuning performance of BERT.
FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images
In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images.
Deep Attentive Sentence Ordering Network
In this paper, we propose a novel deep attentive sentence ordering network (referred as ATTOrderNet) which integrates self-attention mechanism with LSTMs in the encoding of input sentences.
Unfolding Hidden Barriers by Active Enhanced Sampling
We introduce an active learning scheme that consists of a parametric CV learner based on deep neural network and a CV-based enhanced sampler.
On Consideration of Content Preference and Sharing Willingness in D2D Assisted Offloading
Furthermore, an alternative group optimization (AGO) algorithm is proposed to solve the general case of the optimization problem.
Networking and Internet Architecture
