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Found 67 papers, 20 papers with code
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
Our approach only needs to modify the input image and can work with any network to improve its performance.
Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications
Compact neural networks are inclined to exploit "sparsely-connected" convolutions such as depthwise convolution and group convolution for employment in mobile applications.
AgentBench: Evaluating LLMs as Agents
We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
We present WebGLM, a web-enhanced question-answering system based on the General Language Model (GLM).
Geometric Transformer for Fast and Robust Point Cloud Registration
Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds.
GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer
They seek correspondences over downsampled superpoints, which are then propagated to dense points.
Ranked #5 on
Point Cloud Registration
on FP-O-H
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources.
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Large language models (LLMs) have fueled many intelligent agent tasks, such as web navigation -- but most existing agents perform far from satisfying in real-world webpages due to three factors: (1) the versatility of actions on webpages, (2) HTML text exceeding model processing capacity, and (3) the complexity of decision-making due to the open-domain nature of web.
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration
We study the problem of extracting correspondences between a pair of point clouds for registration.
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration
We study the problem of extracting correspondences between a pair of point clouds for registration.
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions.
Rotation-Invariant Transformer for Point Cloud Matching
To this end, we introduce RoITr, a Rotation-Invariant Transformer to cope with the pose variations in the point cloud matching task.
Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration
We first design a local spatial consistency measure over the deformation graph of the point cloud, which evaluates the spatial compatibility only between the correspondences in the vicinity of a graph node.
Training Vision Transformers with Only 2040 Images
Vision Transformers (ViTs) is emerging as an alternative to convolutional neural networks (CNNs) for visual recognition.
Mixup Without Hesitation
In this paper, we find that mixup constantly explores the representation space, and inspired by the exploration-exploitation dilemma in reinforcement learning, we propose mixup Without hesitation (mWh), a concise, effective, and easy-to-use training algorithm.
A Unified Pruning Framework for Vision Transformers
Recently, vision transformer (ViT) and its variants have achieved promising performances in various computer vision tasks.
TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification
We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video.
Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification
Accurate and efficient lumbar spine disease identification is crucial for clinical diagnosis.
Reviving Undersampling for Long-Tailed Learning
In this paper, we aim to enhance the accuracy of the worst-performing categories and utilize the harmonic mean and geometric mean to assess the model's performance.
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i. e. the synthesis of programming language solutions given a natural language problem description.
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization
As it requires a huge number of parameters when exposed to high dimensional inputs in video detection and classification, there is a grand challenge to develop a compact yet accurate video comprehension at terminal devices.
Fast K-Means Clustering with Anderson Acceleration
We propose a novel method to accelerate Lloyd's algorithm for K-Means clustering.
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples.
S-OHEM: Stratified Online Hard Example Mining for Object Detection
S-OHEM exploits OHEM with stratified sampling, a widely-adopted sampling technique, to choose the training examples according to this influence during hard example mining, and thus enhance the performance of object detectors.
Online Convex Optimization with Stochastic Constraints
This paper considers online convex optimization (OCO) with stochastic constraints, which generalizes Zinkevich's OCO over a known simple fixed set by introducing multiple stochastic functional constraints that are i. i. d.
A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
For processing static data in large batch sizes, the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9. 5x higher energy efficiency.
A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing
In this paper, we develop a binary convolutional encoder-decoder network (B-CEDNet) for natural scene text processing (NSTP).
A Low Complexity Algorithm with $O(\sqrt{T})$ Regret and $O(1)$ Constraint Violations for Online Convex Optimization with Long Term Constraints
That prior work proposes an algorithm to achieve $O(\sqrt{T})$ regret and $O(T^{3/4})$ constraint violations for general problems and another algorithm to achieve an $O(T^{2/3})$ bound for both regret and constraint violations when the constraint set can be described by a finite number of linear constraints.
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning
Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker.
MOHONE: Modeling Higher Order Network Effects in KnowledgeGraphs via Network Infused Embeddings
First, we introduce methods that learn network representations of entities in the knowledge graph capturing these varied aspects of similarity.
Solving Non-smooth Constrained Programs with Lower Complexity than \mathcal{O}(1/\varepsilon): A Primal-Dual Homotopy Smoothing Approach
In this paper, we show that by leveraging a local error bound condition on the dual function, the proposed algorithm can achieve a better primal convergence time of $\mathcal{O}\l(\varepsilon^{-2/(2+\beta)}\log_2(\varepsilon^{-1})\r)$, where $\beta\in(0, 1]$ is a local error bound parameter.
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
Recent developments on large-scale distributed machine learning applications, e. g., deep neural networks, benefit enormously from the advances in distributed non-convex optimization techniques, e. g., distributed Stochastic Gradient Descent (SGD).
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization
We show that for stochastic non-convex optimization under the P-L condition, the classical data-parallel SGD with exponentially increasing batch sizes can achieve the fastest known $O(1/(NT))$ convergence with linear speedup using only $\log(T)$ communication rounds.
A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers
In this paper, we propose a new parallel multi-block stochastic ADMM for distributed stochastic optimization, where each node is only required to perform simple stochastic gradient descent updates.
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System
In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS).
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression
The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy.
Crude oil price forecasting incorporating news text
Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long text to discover knowledge from them.
Dynamic Phase Diagram of an Orthogonal Spin Torque device: Topological Category
The magnetization evolution of the free layer in an orthogonal spin-torque device is studied based on a macrospin model.
Mesoscale and Nanoscale Physics
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation
Real-time understanding in video is crucial in various AI applications such as autonomous driving.
Robust Attacks on Deep Learning Face Recognition in the Physical World
Deep neural networks (DNNs) have been increasingly used in face recognition (FR) systems.
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning
Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation
Experimental results on five benchmarks and four cross-domain datasets show the lexicon-based graph convolutional network successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010).
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation
Pose estimation of 3D objects in monocular images is a fundamental and long-standing problem in computer vision.
Task-Specific Context Decoupling for Object Detection
Classification and localization are two main sub-tasks in object detection.
Online Camera-to-ground Calibration for Autonomous Driving
Online camera-to-ground calibration is to generate a non-rigid body transformation between the camera and the road surface in a real-time manner.
TOPLight: Lightweight Neural Networks With Task-Oriented Pretraining for Visible-Infrared Recognition
Visible-infrared recognition (VI recognition) is a challenging task due to the enormous visual difference across heterogeneous images.
G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering
Through empirical evaluation, comparing G$^2$uardFL with cutting-edge defenses, such as FLAME (USENIX Security 2022) [28] and DeepSight (NDSS 2022) [36], against various backdoor attacks including 3DFed (SP 2023) [20], our results demonstrate its significant effectiveness in mitigating backdoor attacks while having a negligible impact on the aggregated model's performance on benign samples (i. e., the primary task performance).
Open, Closed, or Small Language Models for Text Classification?
Recent advancements in large language models have demonstrated remarkable capabilities across various NLP tasks.
Dynamic Hyperbolic Attention Network for Fine Hand-object Reconstruction
In this work, we propose the first precise hand-object reconstruction method in hyperbolic space, namely Dynamic Hyperbolic Attention Network (DHANet), which leverages intrinsic properties of hyperbolic space to learn representative features.
Modality Unifying Network for Visible-Infrared Person Re-Identification
Visible-infrared person re-identification (VI-ReID) is a challenging task due to large cross-modality discrepancies and intra-class variations.
Contrastive Continual Multi-view Clustering with Filtered Structural Fusion
Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer with fixed size to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning.
Forest aboveground biomass estimation using GEDI and earth observation data through attention-based deep learning
AU-FC achieved intermediate R2 of 0. 64, RMSE of 44. 92 Mgha-1, and bias of -0. 56 Mg ha-1, outperforming RF but underperforming AU model using spatial information.
Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data
Consequently, we proposed a novel deep learning framework termed the multi-modal attention remote sensing network (MARSNet) to estimate forest dominant height by extrapolating dominant height derived from GEDI, using Setinel-1 data, ALOS-2 PALSAR-2 data, Sentinel-2 optical data and ancillary data.
Data-Driven Modelling for Harmonic Current Emission in Low-Voltage Grid Using MCReSANet with Interpretability Analysis
The results by feature importance analysis show the detailed relationships between each order of harmonic voltage and current in the distribution system.
Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types
This model leverages exogenous gene network information to facilitate the clustering process, generating discriminative representations.
Federated Continual Learning via Knowledge Fusion: A Survey
The key objective of FCL is to fuse heterogeneous knowledge from different clients and retain knowledge of previous tasks while learning on new ones.
Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human Elbows
We compared the elbow movement controlled by an RL agent with the motion of an actual human elbow in terms of the impedance identified in torque-perturbation experiments.
Middleware for LLMs: Tools Are Instrumental for Language Agents in Complex Environments
The applications of large language models (LLMs) have expanded well beyond the confines of text processing, signaling a new era where LLMs are envisioned as generalist language agents capable of operating within complex real-world environments.
APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
Large Language Models (LLMs) have greatly advanced the natural language processing paradigm.
MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images
MatchU is a generic approach that fuses 2D texture and 3D geometric cues for 6D pose prediction of unseen objects.
Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)
Hence, this paper presents the concept of Adaptable CNC (ACNC) as an autonomous Machine Learning (ML)-aided mechanism crafted for the joint orchestration of computing and network resources, catering to dynamic and voluminous user requests with stringent requirements.
A Comparative Study of Machine Learning Models Predicting Energetics of Interacting Defects
Furthermore, with synthetic data generate from cluster expansion model at near-DFT levels, we obtained enlarged dataset to assess the demands on data for training accurate prediction models using graph neural networks for systems featuring interacting defects.
InfiCoder-Eval: Systematically Evaluating the Question-Answering Capabilities of Code Large Language Models
Large Language Models for understanding and generating code (code LLMs) have witnessed tremendous progress in recent years.
