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Found 23 papers, 6 papers with code
Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy
The interactive effect is significant in the Chinese stock market, exacerbating the abnormal market volatilities and risk contagion.
Improving Weak-to-Strong Generalization with Scalable Oversight and Ensemble Learning
In the first phase, based on human supervision, the quality of weak supervision is enhanced through a combination of scalable oversight and ensemble learning, reducing the capability gap between weak teachers and strong students.
CDEval: A Benchmark for Measuring the Cultural Dimensions of Large Language Models
This benchmark serves as a valuable resource for cultural studies in LLMs, paving the way for more culturally aware and sensitive models.
AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
Despite making significant progress in multi-modal tasks, current Multi-modal Large Language Models (MLLMs) encounter the significant challenge of hallucinations, which may lead to harmful consequences.
Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets
Due to the limited information about emerging diseases, symptoms are hard to be noticed and recognized, so that the window for clinical intervention could be ignored.
Recovering a Molecule's 3D Dynamics from Liquid-phase Electron Microscopy Movies
To our knowledge, our work is the first attempt to directly recover 3D structures of a temporally-varying particle from liquid-phase EM movies.
Cryogenic Electron Microscopy (cryo-EM)
Object Reconstruction
+1
ACE-HetEM for ab initio Heterogenous Cryo-EM 3D Reconstruction
Unfortunately, standard amortized-inference-based methods with entangled latent spaces have difficulty learning the distribution of conformations and poses from cryo-EM images.
FFF: Fragments-Guided Flexible Fitting for Building Complete Protein Structures
This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure.
You talk what you read: Understanding News Comment Behavior by Dispositional and Situational Attribution
Many news comment mining studies are based on the assumption that comment is explicitly linked to the corresponding news.
Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction
Many works employed prompt tuning methods to automatically optimize prompt queries and extract the factual knowledge stored in Pretrained Language Models.
Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework
In this paper, we propose the Adversarial Decoupling Augmentation Framework (ADAF), addressing these issues by targeting the image-text fusion module to enhance the defensive performance of facial privacy protection algorithms.
Boosted ab initio Cryo-EM 3D Reconstruction with ACE-EM
The central problem in cryo-electron microscopy (cryo-EM) is to recover the 3D structure from noisy 2D projection images which requires estimating the missing projection angles (poses).
Kainate receptor modulation by NETO2
Glutamate-gated kainate receptors (KARs) are ubiquitous in the central nervous system of vertebrates, mediate synaptic transmission on post-synapse, and modulate transmitter release on pre-synapse.
FFF: Fragment-Guided Flexible Fitting for Building Complete Protein Structures
This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure.
Universal Backdoor Attacks Detection via Adaptive Adversarial Probe
Most detection methods are designed to verify whether a model is infected with presumed types of backdoor attacks, yet the adversary is likely to generate diverse backdoor attacks in practice that are unforeseen to defenders, which challenge current detection strategies.
Detecting fake news by enhanced text representation with multi-EDU-structure awareness
Finally, the two EDU representations are incorporated as the enhanced text representation for fake news detection, using a gated recursive unit combined with a global attention mechanism.
Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data
Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis.
Dynamic Underwater Acoustic Channel Tracking for Correlated Rapidly Time-varying Channels
A model based on the underwater acoustic channel's correlation can be used as the state-space model in the Kalman filter to improve the underwater acoustic channel tracking compared that without a model.
Path-specific Underwater Acoustic Channel Tracking and its Application in Passive Time Reversal Mirror
With the path-specific parameters obtained by the proposed channel tracking, the proposed PTRM can not only match the time dispersion as conventional PTRM, but also the doubly-spread channel, since the path-specific delay and Doppler scaler factor can help to match the channel in both time and frequency domain.
SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection
In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations.
Adaptive Context Network for Scene Parsing
Recent works attempt to improve scene parsing performance by exploring different levels of contexts, and typically train a well-designed convolutional network to exploit useful contexts across all pixels equally.
Ranked #72 on
Semantic Segmentation
on ADE20K val
Stacked Deconvolutional Network for Semantic Segmentation
In SDN, multiple shallow deconvolutional networks, which are called as SDN units, are stacked one by one to integrate contextual information and guarantee the fine recovery of localization information.
Ranked #4 on
Semantic Segmentation
on PASCAL VOC 2012 test
Quantized Convolutional Neural Networks for Mobile Devices
Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks.
