Search Results for author:
Found 122 papers, 51 papers with code
On the Powerball Method for Optimization
We propose a new method to accelerate the convergence of optimization algorithms.
Review Networks for Caption Generation
We propose a novel extension of the encoder-decoder framework, called a review network.
Inverse Power Flow Problem
We show that the admittance matrix can be uniquely identified from a sequence of measurements corresponding to different steady states when every node in the system is equipped with a measurement device, and a Kron-reduced admittance matrix can be determined even if some nodes in the system are not monitored (hidden nodes).
Words or Characters? Fine-grained Gating for Reading Comprehension
Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension.
Ranked #50 on
Question Answering
on SQuAD1.1 dev
Joint Hand Detection and Rotation Estimation by Using CNN
Hand detection is essential for many hand related tasks, e. g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction.
Understanding Convolution for Semantic Segmentation
This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation.
Ranked #20 on
Semantic Segmentation
on PASCAL VOC 2012 test
Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification
We report on CMU Informedia Lab's system used in Google's YouTube 8 Million Video Understanding Challenge.
On Identification of Distribution Grids
Large-scale integration of distributed energy resources into residential distribution feeders necessitates careful control of their operation through power flow analysis.
Face Attention Network: An Effective Face Detector for the Occluded Faces
The performance of face detection has been largely improved with the development of convolutional neural network.
Ranked #1 on
Occluded Face Detection
on MAFA
SFace: An Efficient Network for Face Detection in Large Scale Variations
A new dataset called 4K-Face is also introduced to evaluate the performance of face detection with extreme large scale variations.
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events.
3D Ego-Pose Estimation via Imitation Learning
Motivated by this, we propose a novel control-based approach to model human motion with physics simulation and use imitation learning to learn a video-conditioned control policy for ego-pose estimation.
Data-driven Discovery of Cyber-Physical Systems
Cyber-physical systems (CPSs) embed software into the physical world.
A deep learning-based remaining useful life prediction approach for bearings
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs).
A General End-to-end Diagnosis Framework for Manufacturing Systems
A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications.
Bridging the Gap Between Computational Photography and Visual Recognition
From the observed results, it is evident that we are in the early days of building a bridge between computational photography and visual recognition, leaving many opportunities for innovation in this area.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis
However, for diverse working conditions in the industry, deep learning suffers two difficulties: one is that the well-defined (source domain) and new (target domain) datasets are with different feature distributions; another one is the fact that insufficient or no labelled data in target domain significantly reduce the accuracy of fault diagnosis.
Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA)
Our final algorithm, mini-batch gradient descent with regularization, DropRule and AdaBound (MBGD-RDA), can achieve fast convergence in training TSK fuzzy systems, and also superior generalization performance in testing.
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series
This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases.
UG$^{2+}$ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
The UG$^{2+}$ challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
Generative Hybrid Representations for Activity Forecasting with No-Regret Learning
Automatically reasoning about future human behaviors is a difficult problem but has significant practical applications to assistive systems.
Ego-Pose Estimation and Forecasting as Real-Time PD Control
We propose the use of a proportional-derivative (PD) control based policy learned via reinforcement learning (RL) to estimate and forecast 3D human pose from egocentric videos.
Diverse Trajectory Forecasting with Determinantal Point Processes
To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP).
Ranked #5 on
Human Pose Forecasting
on HumanEva-I
ABD-Net: Attentive but Diverse Person Re-Identification
Attention mechanism has been shown to be effective for person re-identification (Re-ID).
Ranked #16 on
Person Re-Identification
on Market-1501-C
Machine Discovery of Partial Differential Equations from Spatiotemporal Data
The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data.
Is There Mode Collapse? A Case Study on Face Generation and Its Black-box Calibration
Generative adversarial networks (GANs) nowadays are capable of producing im-ages of incredible realism.
PowerSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization
In this paper, we propose a novel technique for improving the stochastic gradient descent (SGD) method to train deep networks, which we term \emph{PowerSGD}.
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
Following these guidelines, we design our Fully Convolutional Siamese tracker++ (SiamFC++) by introducing both classification and target state estimation branch(G1), classification score without ambiguity(G2), tracking without prior knowledge(G3), and estimation quality score(G4).
Ranked #2 on
Visual Object Tracking
on VOT2017/18
(using extra training data)
Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification
Many real-world applications, such as city-scale traffic monitoring and control, requires large-scale re-identification.
A Practical Solution for SAR Despeckling With Adversarial Learning Generated Speckled-to-Speckled Images
In this letter, we aim to address a synthetic aperture radar (SAR) despeckling problem with the necessity of neither clean (speckle-free) SAR images nor independent speckled image pairs from the same scene, and a practical solution for SAR despeckling (PSD) is proposed.
In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation
This work addresses the above two shortcomings of triplet loss, extending its effectiveness to large-scale ReID datasets with potentially noisy labels.
State-Aware Tracker for Real-Time Video Object Segmentation
For higher efficiency, SAT takes advantage of the inter-frame consistency and deals with each target object as a tracklet.
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm.
PTP: Parallelized Tracking and Prediction with Graph Neural Networks and Diversity Sampling
We evaluate on KITTI and nuScenes datasets showing that our method with socially-aware feature learning and diversity sampling achieves new state-of-the-art performance on 3D MOT and trajectory prediction.
DLow: Diversifying Latent Flows for Diverse Human Motion Prediction
To obtain samples from a pretrained generative model, most existing generative human motion prediction methods draw a set of independent Gaussian latent codes and convert them to motion samples.
Ranked #1 on
Human Pose Forecasting
on AMASS
(APD metric)
BoostTree and BoostForest for Ensemble Learning
Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance.
Optical Non-Line-of-Sight Physics-based 3D Human Pose Estimation
We bring together a diverse set of technologies from NLOS imaging, human pose estimation and deep reinforcement learning to construct an end-to-end data processing pipeline that converts a raw stream of photon measurements into a full 3D human pose sequence estimate.
Semi-Supervised Cervical Dysplasia Classification With Learnable Graph Convolutional Network
In this paper, we propose a novel and more flexible GCN model with a feature encoder that adaptively updates the adjacency matrix during learning and demonstrate that this model design leads to improved performance.
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach
Spatial crowdsourcing (SC) utilizes the potential of a crowd to accomplish certain location based tasks.
Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
Our approach is the first humanoid control method that successfully learns from a large-scale human motion dataset (Human3. 6M) and generates diverse long-term motions.
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i. e., learning binary classifiers from a large amount of unlabeled data and a few labeled positive examples.
AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses
Based on the prediction of each anchor template, we propose to aggregate the results, which can reduce the landmark uncertainty due to the large poses.
Ranked #1 on
Face Alignment
on AFLW-Full
(Mean NME metric)
On Deep Unsupervised Active Learning
Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating.
Efficient Non-Line-of-Sight Imaging from Transient Sinograms
Non-line-of-sight (NLOS) imaging techniques use light that diffusely reflects off of visible surfaces (e. g., walls) to see around corners.
AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation
Such simplification limits the fusion of information at different scales and fails to maintain high-resolution representations.
End-to-End 3D Multi-Object Tracking and Trajectory Forecasting
To evaluate this hypothesis, we propose a unified solution for 3D MOT and trajectory forecasting which also incorporates two additional novel computational units.
PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning
While machine learning is increasingly used in this field, the resulting large-scale collection of user private information has reinvigorated the privacy debate, considering dozens of data breach incidents every year caused by unauthorized hackers, and (potentially even more) information misuse/abuse by authorized parties.
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.
Automated data extraction of bar chart raster images
Objective: To develop software utilizing optical character recognition toward the automatic extraction of data from bar charts for meta-analysis.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Kinematics-Guided Reinforcement Learning for Object-Aware 3D Ego-Pose Estimation
We propose a method for incorporating object interaction and human body dynamics into the task of 3D ego-pose estimation using a head-mounted camera.
Causal inference using deep neural networks
Causal inference from observation data is a core problem in many scientific fields.
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects.
Ranked #13 on
Few-Shot Object Detection
on MS-COCO (30-shot)
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
Instead, we would prefer a method that allows an agent's state at one time to directly affect another agent's state at a future time.
Ranked #10 on
Trajectory Prediction
on ETH/UCY
SimPoE: Simulated Character Control for 3D Human Pose Estimation
Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e. g., joint torques) of the character to advance the current-frame pose estimate to the pose estimate of the next frame.
Ranked #229 on
3D Human Pose Estimation
on Human3.6M
LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images
Thus, it is not ideal to apply most existing segmentation methods as they are designed for either 2D or 3D images.
Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation
By comparing the pose instructed by the kinematic model against the pose generated by the dynamics model, we can use their misalignment to further improve the kinematic model.
Egocentric Pose Estimation
Human-Object Interaction Detection
+2
DeceFL: A Principled Decentralized Federated Learning Framework
Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client.
Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Study
Existing diversity tests of samples from GANs are usually conducted qualitatively on a small scale, and/or depends on the access to original training data as well as the trained model parameters.
Temporal Knowledge Consistency for Unsupervised Visual Representation Learning
The instance discrimination paradigm has become dominant in unsupervised learning.
Font Completion and Manipulation by Cycling Between Multi-Modality Representations
The novel graph constructor maps a glyph's latent code to its graph representation that matches expert knowledge, which is trained to help the translation task.
POI Alias Discovery in Delivery Addresses using User Locations
People often refer to a place of interest (POI) by an alias.
Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis
Edge devices in federated learning usually have much more limited computation and communication resources compared to servers in a data center.
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
Specifically, we learn a conditional policy that, in an episode, first applies a sequence of transform actions to modify an agent's skeletal structure and joint attributes, and then applies control actions under the new design.
Causal Effect Estimation using Variational Information Bottleneck
A typical approach is to formulate causal inference as a supervised learning problem and so counterfactual could be predicted.
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering
In this paper, we propose a new deep subspace clustering framework, motivated by the energy-based models.
FBNet: Feature Balance Network for Urban-Scene Segmentation
In this paper, we present a novel add-on module, named Feature Balance Network (FBNet), to eliminate the feature camouflage in urban-scene segmentation.
Deep Unsupervised Active Learning on Learnable Graphs
To make the learnt graph structure more stable and effective, we take into account $k$-nearest neighbor graph as a priori, and learn a relation propagation graph structure.
GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras
Since the joint reconstruction of human motions and camera poses is underconstrained, we propose a global trajectory predictor that generates global human trajectories based on local body movements.
Ranked #1 on
Global 3D Human Pose Estimation
on EMDB
Boosting Contrastive Learning with Relation Knowledge Distillation
We delve into this problem and find that the lightweight model is prone to collapse in semantic space when simply performing instance-wise contrast.
On Almost Sure Convergence Rates of Stochastic Gradient Methods
We further provide last-iterate almost sure convergence rates analysis for stochastic gradient methods on weakly convex smooth functions, in contrast with most existing results in the literature that only provide convergence in expectation for a weighted average of the iterates.
Syntax-Aware Network for Handwritten Mathematical Expression Recognition
In this paper, we propose a simple and efficient method for HMER, which is the first to incorporate syntax information into an encoder-decoder network.
Adaptive Divergence-based Non-negative Latent Factor Analysis
High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications with complex interactions among numerous nodes, which are commonly non-negative for representing the inherent non-negativity of node interactions.
Online No-regret Model-Based Meta RL for Personalized Navigation
Our theoretical analysis shows that our method is a no-regret algorithm and we provide the convergence rate in the agnostic setting.
Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering
Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views.
Unified Simulation, Perception, and Generation of Human Behavior
Understanding and modeling human behavior is fundamental to almost any computer vision and robotics applications that involve humans.
A Sampling Theorem for Exact Identification of Continuous-time Nonlinear Dynamical Systems
The necessary and sufficient condition is proposed -- which is built from Koopman operator -- to the exact identification of the CT system from sampled data.
PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis
A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numerous nodes.
Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression
Symbolic Regression (SR) is a type of regression analysis to automatically find the mathematical expression that best fits the data.
Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation
We also propose a new mixture-of-experts (MoE) based decoder, which treats the feature maps from the encoder as experts and selects a suitable set of expert features to predict the label for each pixel.
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks
Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teacher GNN into a shallower student GNN.
From Universal Humanoid Control to Automatic Physically Valid Character Creation
Second, we use a design-and-control framework to optimize a humanoid's physical attributes to find body designs that can better imitate the pre-specified human motion sequence(s).
Embodied Scene-aware Human Pose Estimation
Since 2D third-person observations are coupled with the camera pose, we propose to disentangle the camera pose and use a multi-step projection gradient defined in the global coordinate frame as the movement cue for our embodied agent.
Ranked #306 on
3D Human Pose Estimation
on Human3.6M
SearchMorph:Multi-scale Correlation Iterative Network for Deformable Registration
Deformable image registration can obtain dynamic information about images, which is of great significance in medical image analysis.
Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration
To this end, we propose a Face Restoration Searching Network (FRSNet) to adaptively search the suitable feature extraction architecture within our specified search space, which can directly contribute to the restoration quality.
Robust Knowledge Adaptation for Dynamic Graph Neural Networks
By this means, we can adaptively propagate knowledge to other nodes for learning robust node embedding representations.
When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition
Recently, most handwritten mathematical expression recognition (HMER) methods adopt the encoder-decoder networks, which directly predict the markup sequences from formula images with the attention mechanism.
A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model
High-dimensional and incomplete (HDI) data holds tremendous interactive information in various industrial applications.
Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization
Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix.
RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control
We present a system for collision-free control of a robot manipulator that uses only RGB views of the world.
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
What's more, to fully exploit Beta Representation, a novel pipeline Beta R-CNN equipped with BetaHead and BetaMask is proposed, leading to high detection performance in occluded and crowded scenes.
Ranked #11 on
Pedestrian Detection
on CityPersons
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters.
Ranked #5 on
Link Prediction
on UMLS
1st Place Solutions for UG2+ Challenge 2022 ATMOSPHERIC TURBULENCE MITIGATION
In this technical report, we briefly introduce the solution of our team ''summer'' for Atomospheric Turbulence Mitigation in UG$^2$+ Challenge in CVPR 2022.
Prototype as Query for Few Shot Semantic Segmentation
In this way, the spatial details can be better captured and the semantic features of target class in the query image can be focused.
Ranked #11 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs
Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module; b) Building the residual connection and weighted representation propagation to obtain high representation capacity; and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG.
PhysDiff: Physics-Guided Human Motion Diffusion Model
Specifically, we propose a physics-based motion projection module that uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically-plausible motion.
Learning Human Dynamics in Autonomous Driving Scenarios
In this work, we propose a holistic framework for learning physically plausible human dynamics from real driving scenarios, narrowing the gap between real and simulated human behavior in safety-critical applications.
Almost Sure Saddle Avoidance of Stochastic Gradient Methods without the Bounded Gradient Assumption
We prove that various stochastic gradient descent methods, including the stochastic gradient descent (SGD), stochastic heavy-ball (SHB), and stochastic Nesterov's accelerated gradient (SNAG) methods, almost surely avoid any strict saddle manifold.
EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
Predicting the binding sites of the target proteins plays a fundamental role in drug discovery.
Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion
We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals.
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning
Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on downstream tasks.
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations
Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and thus the possibility of instant rendering on new scenes.
Shared Growth of Graph Neural Networks via Prompted Free-direction Knowledge Distillation
Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper teacher GNN into a shallower student GNN.
DREAM: Domain-free Reverse Engineering Attributes of Black-box Model
There is a crucial limitation: these works assume the dataset used for training the target model to be known beforehand and leverage this dataset for model attribute attack.
TREA: Tree-Structure Reasoning Schema for Conversational Recommendation
TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results.
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition
Then we design a semantic aware module (SAM), which projects the visual and classification feature into semantic space.
FIMO: A Challenge Formal Dataset for Automated Theorem Proving
We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems.
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models.
Learning to Generate Parameters of ConvNets for Unseen Image Data
Typical Convolutional Neural Networks (ConvNets) depend heavily on large amounts of image data and resort to an iterative optimization algorithm (e. g., SGD or Adam) to learn network parameters, which makes training very time- and resource-intensive.
PACE: Human and Camera Motion Estimation from in-the-wild Videos
This design combines the strengths of SLAM and motion priors, which leads to significant improvements in human and camera motion estimation.
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations
Imitation Learning (IL) aims to discover a policy by minimizing the discrepancy between the agent's behavior and expert demonstrations.
GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning
Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations.
AGG: Amortized Generative 3D Gaussians for Single Image to 3D
To overcome these challenges, we introduce an Amortized Generative 3D Gaussian framework (AGG) that instantly produces 3D Gaussians from a single image, eliminating the need for per-instance optimization.
An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis
However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm only considers the current learning error to compute the stochastic gradient without considering the historical and future state of the learning error.
Tensor Graph Convolutional Network for Dynamic Graph Representation Learning
Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios.
Preparing Lessons for Progressive Training on Language Models
The rapid progress of Transformers in artificial intelligence has come at the cost of increased resource consumption and greenhouse gas emissions due to growing model sizes.
FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models
To address the limited data resources and reduce the annotation cost, we introduce FinLLMs, a method for generating financial question-answering data based on common financial formulas using Large Language Models.
Structured Entity Extraction Using Large Language Models
Recent advances in machine learning have significantly impacted the field of information extraction, with Large Language Models (LLMs) playing a pivotal role in extracting structured information from unstructured text.
Measuring Vision-Language STEM Skills of Neural Models
Compared to existing datasets that often focus on examining expert-level ability, our dataset includes fundamental skills and questions designed based on the K-12 curriculum.
On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving
However, the oversized neural networks render them impractical for deployment on resource-constrained systems, which unavoidably requires more computational time and resources during reference. To handle this, knowledge distillation offers a promising approach that compresses models by enabling a smaller student model to learn from a larger teacher model.
Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases
We proposed an end-to-end system design towards utilizing Retrieval Augmented Generation (RAG) to improve the factual accuracy of Large Language Models (LLMs) for domain-specific and time-sensitive queries related to private knowledge-bases.
Measuring Social Norms of Large Language Models
This enables the direct comparison of the social understanding of large language models to humans, more specifically, elementary students.
