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Found 52 papers, 12 papers with code
RSGT: Relational Structure Guided Temporal Relation Extraction
In this paper, we propose RSGT: Relational Structure Guided Temporal Relation Extraction to extract the relational structure features that can fit for both inter-sentence and intra-sentence relations.
Ranked #1 on
Temporal Relation Classification
on MATRES
Natural Language Understanding
Temporal Relation Classification
BNS-Net: A Dual-channel Sarcasm Detection Method Considering Behavior-level and Sentence-level Conflicts
Sarcasm detection is a binary classification task that aims to determine whether a given utterance is sarcastic.
ORES: Open-vocabulary Responsible Visual Synthesis
In this work, we formalize a new task, Open-vocabulary Responsible Visual Synthesis (ORES), where the synthesis model is able to avoid forbidden visual concepts while allowing users to input any desired content.
NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation
In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation.
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
To this end, We build a system called \textbf{Visual ChatGPT}, incorporating different Visual Foundation Models, to enable the user to interact with ChatGPT by 1) sending and receiving not only languages but also images 2) providing complex visual questions or visual editing instructions that require the collaboration of multiple AI models with multi-steps.
SATBA: An Invisible Backdoor Attack Based On Spatial Attention
Our attack leverages spatial attention mechanism to extract data features and generate invisible trigger patterns that are correlated with clean data.
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images
3D photography renders a static image into a video with appealing 3D visual effects.
Ranked #1 on
Image Outpainting
on MSCOCO
RD-NAS: Enhancing One-shot Supernet Ranking Ability via Ranking Distillation from Zero-cost Proxies
In this paper, we propose Ranking Distillation one-shot NAS (RD-NAS) to enhance ranking consistency, which utilizes zero-cost proxies as the cheap teacher and adopts the margin ranking loss to distill the ranking knowledge.
Progressive Meta-Pooling Learning for Lightweight Image Classification Model
Practical networks for edge devices adopt shallow depth and small convolutional kernels to save memory and computational cost, which leads to a restricted receptive field.
Unsupervised Knowledge Graph Construction and Event-centric Knowledge Infusion for Scientific NLI
Therefore, we propose an unsupervised knowledge graph construction method to build a scientific knowledge graph (SKG) without any labeled data.
Radar-enabled ambient backscatter communications
In this work, we exploit the radar clutter (i. e., the ensemble of echoes generated by the terrain and/or the surrounding objects in response to the signal emitted by a radar transmitter) as a carrier signal to enable an ambient backscatter communication from a source (tag) to a destination (reader).
MIMO OFDM Dual-Function Radar-Communication Under Error Rate and Beampattern Constraints
In this work we consider a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system, which senses multiple spatial directions and serves multiple users.
Learning Invariant Representation with Consistency and Diversity for Semi-supervised Source Hypothesis Transfer
Semi-supervised domain adaptation (SSDA) aims to solve tasks in target domain by utilizing transferable information learned from the available source domain and a few labeled target data.
Two stages for visual object tracking
Then more accurate tracking results are obtained by segmentation module given the coarse state estimation in the first stage.
Coarse- and fine-scale geometric information content of Multiclass Classification and implied Data-driven Intelligence
Under any Multiclass Classification (MCC) setting defined by a collection of labeled point-cloud specified by a feature-set, we extract only stochastic partial orderings from all possible triplets of point-cloud without explicitly measuring the three cloud-to-cloud distances.
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers.
Towards Extremely Compact RNNs for Video Recognition with Fully Decomposed Hierarchical Tucker Structure
Although various prior works have been proposed to reduce the RNN model sizes, executing RNN models in resource-restricted environments is still a very challenging problem.
Privacy-preserving Channel Estimation in Cell-free Hybrid Massive MIMO Systems
We consider a cell-free hybrid massive multiple-input multiple-output (MIMO) system with $K$ users and $M$ access points (APs), each with $N_a$ antennas and $N_r< N_a$ radio frequency (RF) chains.
Low-Rank Matrix Completion
Information Theory
Signal Processing
Information Theory
Unraveling S&P500 stock volatility and networks -- An encoding-and-decoding approach
In the real data applications, we introduce the application of our approach in forecasting stock returns.
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale
The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models.
ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar
Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in super-resolution stepped frequency radar angle-range-doppler imaging.
Multi-mode OAM Radio Waves: Generation, Angle of Arrival Estimation and Reception With UCAs
To solve these problems, we propose an overall scheme of the line-of-sight multi-carrier and multi-mode OAM (LoS MCMM-OAM) communication based on uniform circular arrays (UCAs).
From learning gait signatures of many individuals to reconstructing gait dynamics of one single individual
With proper color-coding and stacking, we reconstruct and represent an individual's gait dynamics via a 3D cylinder to collectively reveal universal deterministic and stochastic structural patterns on centisecond (10 milliseconds) scale across all rhythmic cycles.
Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition
Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling.
GEVO: GPU Code Optimization using Evolutionary Computation
If kernel output accuracy is relaxed to tolerate up to 1% error, GEVO can find kernel variants that outperform the baseline version by an average of 51. 08%.
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
Large-scale training is important to ensure high performance and accuracy of machine-learning models.
Distributed, Parallel, and Cluster Computing 68T05, 68M10 H.3.3; I.2.6; C.2.1
Progressive Local Filter Pruning for Image Retrieval Acceleration
To verify this, we evaluate our method on two widely-used image retrieval datasets, i. e., Oxford5k and Paris6K, and one person re-identification dataset, i. e., Market-1501.
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure.
Distributed, Parallel, and Cluster Computing
Language Model-Driven Unsupervised Neural Machine Translation
We present the detailed mathematical construction of our method.
SubCharacter Chinese-English Neural Machine Translation with Wubi encoding
However, when the translation task involves Chinese, semantic granularity remains at the word and character level, so there is still need more fine-grained translation model of Chinese.
mmWave/THz Channel Estimation Using Frequency-Selective Atomic Norm Minimization
We propose a MIMO channel estimation method for millimeter-wave (mmWave) and terahertz (THz) systems based on frequency-selective atomic norm minimization (FS-ANM).
Exploiting Parallelism Opportunities with Deep Learning Frameworks
State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers.
Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks
Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities.
The Architectural Implications of Facebook's DNN-based Personalized Recommendation
The widespread application of deep learning has changed the landscape of computation in the data center.
Deep Learning Recommendation Model for Personalization and Recommendation Systems
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
Numerical results are illustrated to demonstrate that efficient policies can be learned at each user, and performance of the proposed DDPG based decentralized strategy outperforms the conventional deep Q-network (DQN) based discrete power control strategy and some other greedy strategies with reduced computation cost.
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
The application of deep learning techniques resulted in remarkable improvement of machine learning models.
Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings
In case the observed data have a low intrinsic dimensionality, we learn a submanifold in which the nominal data are embedded and evaluate whether the sequentially acquired data persistently deviate from the nominal submanifold.
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach
Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid.
A Simple and Space Efficient Segment Tree Implementation
The segment tree is an extremely versatile data structure.
Data Structures and Algorithms
An Online Ride-Sharing Path Planning Strategy for Public Vehicle Systems
As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestions and pollutions for future smart cities.
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion
In this letter, we study the deterministic sampling patterns for the completion of low rank matrix, when corrupted with a sparse noise, also known as robust matrix completion.
Scaled Nuclear Norm Minimization for Low-Rank Tensor Completion
Minimizing the nuclear norm of a matrix has been shown to be very efficient in reconstructing a low-rank sampled matrix.
Rank Determination for Low-Rank Data Completion
Moreover, for both single-view matrix and CP tensor, we are able to show that the obtained upper bound is exactly equal to the unknown rank if the lowest-rank completion is given.
Place recognition: An Overview of Vision Perspective
Place recognition is one of the most fundamental topics in computer vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image.
Fourth-order Tensors with Multidimensional Discrete Transforms
The multidimensional feature and huge volume of big data put urgent requirements to the development of multilinear modeling tools and efficient algorithms.
Numerical Analysis Information Theory Information Theory
Fundamental Conditions for Low-CP-Rank Tensor Completion
Our proposed approach results in characterizing the maximum number of algebraically independent polynomials in terms of a simple geometric structure of the sampling pattern, and therefore we obtain the deterministic necessary and sufficient condition on the sampling pattern for finite completability of the sampled tensor.
Characterization of Deterministic and Probabilistic Sampling Patterns for Finite Completability of Low Tensor-Train Rank Tensor
In this paper, we analyze the fundamental conditions for low-rank tensor completion given the separation or tensor-train (TT) rank, i. e., ranks of unfoldings.
Deterministic and Probabilistic Conditions for Finite Completability of Low-rank Multi-View Data
We provide a deterministic necessary and sufficient condition on the sampling pattern for finite completability.
Deterministic and Probabilistic Conditions for Finite Completability of Low-Tucker-Rank Tensor
We investigate the fundamental conditions on the sampling pattern, i. e., locations of the sampled entries, for finite completability of a low-rank tensor given some components of its Tucker rank.
Low-tubal-rank Tensor Completion using Alternating Minimization
The low-tubal-rank tensor model has been recently proposed for real-world multidimensional data.
Adaptive Sampling of RF Fingerprints for Fine-grained Indoor Localization
In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy.
