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Found 55 papers, 13 papers with code
CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks
Numerous roadside perception datasets have been introduced to propel advancements in autonomous driving and intelligent transportation systems research and development.
Inverse Optimal Control for Linear Quadratic Tracking with Unknown Target States
A computationally efficient and numerically reliable parameter identification algorithm is proposed by equating optimal control strategies with a system of linear equations, and the associated relative error upper bound is derived in terms of data volume and signal-to-noise ratio.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
CalibFormer: A Transformer-based Automatic LiDAR-Camera Calibration Network
The fusion of LiDARs and cameras has been increasingly adopted in autonomous driving for perception tasks.
Adversarial Training for Physics-Informed Neural Networks
AT-PINNs enhance the robustness of PINNs by fine-tuning the model with adversarial samples, which can accurately identify model failure locations and drive the model to focus on those regions during training.
DeeDiff: Dynamic Uncertainty-Aware Early Exiting for Accelerating Diffusion Model Generation
In this work, we propose DeeDiff, an early exiting framework that adaptively allocates computation resources in each sampling step to improve the generation efficiency of diffusion models.
MiChao-HuaFen 1.0: A Specialized Pre-trained Corpus Dataset for Domain-specific Large Models
With the advancement of deep learning technologies, general-purpose large models such as GPT-4 have demonstrated exceptional capabilities across various domains.
A Review of Adversarial Attacks in Computer Vision
Deep neural networks have been widely used in various downstream tasks, especially those safety-critical scenario such as autonomous driving, but deep networks are often threatened by adversarial samples.
DBFed: Debiasing Federated Learning Framework based on Domain-Independent
However, it brings challenges in information security and data security.
SaaFormer: Spectral-spatial Axial Aggregation Transformer for Hyperspectral Image Classification
Consequently, the pixel-wise random sampling approach poses a risk of data leakage.
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack
Thus, we further propose an indicator to measure the local similarity of models, called robustness similitude.
Artificial Neural Network Prediction of COVID-19 Daily Infection Count
It is well known that the confirmed COVID-19 infection is only a fraction of the true fraction.
Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection
LiDAR and Radar are two complementary sensing approaches in that LiDAR specializes in capturing an object's 3D shape while Radar provides longer detection ranges as well as velocity hints.
CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network
We present a novel learning method to predict the cloth deformation for skeleton-based characters with a two-stream network.
Stationary Point Losses for Robust Model
CE loss sharpens the neural network at the decision boundary to achieve a lower loss, rather than pushing the boundary to a more robust position.
TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching
In this paper, we propose TrajMatch -- the first system that can automatically calibrate for roadside LiDARs in both time and space.
Accelerating Dataset Distillation via Model Augmentation
Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but efficient synthetic training datasets from large ones.
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model
To handle this challenge, we propose a novel early exiting strategy for unified visual language models, which allows dynamically skip the layers in encoder and decoder simultaneously in term of input layer-wise similarities with multiple times of early exiting, namely \textbf{MuE}.
Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images
Even though we test the experiments on the skin tumor dataset, our work could also be extended to other medical image detection problems, such as various tumors' classification and prediction, to help and benefit the clinical evaluation and diagnosis of different tumors.
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation
Finally, our analysis shows that the two types of uncertainty provided by \textbf{ADDMU} can be leveraged to characterize adversarial examples and identify the ones that contribute most to model's robustness in adversarial training.
Supervised Parameter Estimation of Neuron Populations from Multiple Firing Events
In this paper, we study an automatic approach of learning the parameters of neuron populations from a training set consisting of pairs of spiking series and parameter labels via supervised learning.
Forecasting SQL Query Cost at Twitter
We propose a SQL query cost predictor service, which employs machine learning techniques to train models from historical query request logs and rapidly forecasts the CPU and memory resource usages of online queries without any computation in a SQL engine.
N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks
We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction.
A Review of Adversarial Attack and Defense for Classification Methods
Despite the efficiency and scalability of machine learning systems, recent studies have demonstrated that many classification methods, especially deep neural networks (DNNs), are vulnerable to adversarial examples; i. e., examples that are carefully crafted to fool a well-trained classification model while being indistinguishable from natural data to human.
Decentralized Composite Optimization with Compression
Decentralized optimization and communication compression have exhibited their great potential in accelerating distributed machine learning by mitigating the communication bottleneck in practice.
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting
As cameras are increasingly deployed in new application domains such as autonomous driving, performing 3D object detection on monocular images becomes an important task for visual scene understanding.
Adversarial Examples Detection with Bayesian Neural Network
In this paper, we propose a new framework to detect adversarial examples motivated by the observations that random components can improve the smoothness of predictors and make it easier to simulate the output distribution of a deep neural network.
$\rm ^{83}Rb$/$\rm ^{83m}Kr$ production and cross-section measurement with 3.4 MeV and 20 MeV proton beams
$\rm ^{83m}Kr$, with a short lifetime, is an ideal calibration source for liquid xenon or liquid argon detectors.
Nuclear Experiment Instrumentation and Detectors
Exploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2 Spike Protein Through Molecular Dynamics Simulation
We performed molecular dynamics simulation on the S protein with a focus on the function of its N-terminal domains (NTDs).
Towards Robustness of Deep Neural Networks via Regularization
Recent studies have demonstrated the vulnerability of deep neural networks against adversarial examples.
Cross-Patch Graph Convolutional Network for Image Denoising
However, the real noisy images in practical are mostly of high resolution rather than the cropped small patches and the vanilla training strategies ignore the cross-patch contextual dependency in the whole image.
Super interference fringes of two-photon photoluminescence in individual Au nanoparticles: the critical role of the intermediate state
Here, we presented two-photon photoluminescence (TPPL) measurements on individual Au nanobipyramids (AuNP) to reveal their ultrafast dynamics by two-pulse excitation on a global time scale ranging from sub-femtosecond to tens of picoseconds.
Optics Mesoscale and Nanoscale Physics Quantum Physics
A deep learning based interactive sketching system for fashion images design
In this work, we propose an interactive system to design diverse high-quality garment images from fashion sketches and the texture information.
Linear Convergent Decentralized Optimization with Compression
Communication compression has become a key strategy to speed up distributed optimization.
Towards Better Opioid Antagonists Using Deep Reinforcement Learning
Naloxone, an opioid antagonist, has been widely used to save lives from opioid overdose, a leading cause for death in the opioid epidemic.
Stationary distributions of persistent ecological systems
We highlight new biological insights by analyzing the stationary distributions of the ecosystems and by seeing how various types of environmental fluctuations influence the long term fate of populations.
A Double Residual Compression Algorithm for Efficient Distributed Learning
Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms.
Defending Against Adversarial Examples by Regularized Deep Embedding
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples.
Improve variational autoEncoder with auxiliary softmax multiclassifier
As a general-purpose generation model, the vanilla VAE can not fit well with various data sets and neural networks with different structures.
On linear convergence of two decentralized algorithms
In addition, we relax the requirement for the objective functions and the mixing matrices.
Learning from Group Comparisons: Exploiting Higher Order Interactions
We study the problem of learning from group comparisons, with applications in predicting outcomes of sports and online games.
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples.
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN
Over the past decade, many Super Resolution techniques have been developed using deep learning.
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
Instead, we model randomness under the framework of Bayesian Neural Network (BNN) to formally learn the posterior distribution of models in a scalable way.
Adversarial 3D Human Pose Estimation via Multimodal Depth Supervision
In this paper, a novel deep-learning based framework is proposed to infer 3D human poses from a single image.
Deep Descriptor Transforming for Image Co-Localization
Reusable model design becomes desirable with the rapid expansion of machine learning applications.
Scalable Demand-Aware Recommendation
In particular for durable goods, time utility is a function of inter-purchase duration within product category because consumers are unlikely to purchase two items in the same category in close temporal succession.
Sequential Person Recognition in Photo Albums with a Recurrent Network
In this work, we propose to model the relational information between people as a sequence prediction task.
Attend in groups: a weakly-supervised deep learning framework for learning from web data
Large-scale datasets have driven the rapid development of deep neural networks for visual recognition.
Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution
More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them.
Mining Mid-level Visual Patterns with Deep CNN Activations
The purpose of mid-level visual element discovery is to find clusters of image patches that are both representative and discriminative.
Iterative Neural Autoregressive Distribution Estimator NADE-k
Training of the neural autoregressive density estimator (NADE) can be viewed as doing one step of probabilistic inference on missing values in data.
Ranked #8 on
Image Generation
on Binarized MNIST
Mid-level Deep Pattern Mining
We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used.
Contextual Hypergraph Modelling for Salient Object Detection
In this work, we model an image as a hypergraph that utilizes a set of hyperedges to capture the contextual properties of image pixels or regions.
Characterness: An Indicator of Text in the Wild
In order to measure the characterness we develop three novel cues that are tailored for character detection, and a Bayesian method for their integration.
