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Found 49 papers, 17 papers with code
Delta-Influence: Unlearning Poisons via Influence Functions
State-of-the-art influence functions, such as EK-FAC, often fail to accurately attribute abnormal model behavior to the specific poisoned training data responsible for the data poisoning attack.
PSPO*: An Effective Process-supervised Policy Optimization for Reasoning Alignment
Process supervision enhances the performance of large language models in reasoning tasks by providing feedback at each step of chain-of-thought reasoning.
METEOR: Evolutionary Journey of Large Language Models from Guidance to Self-Growth
Model evolution enables learning from feedback to refine experiences and update skills, transforming models from having no domain knowledge to becoming domain experts.
A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?
Moreover, we explored the effectiveness of LLMs in detecting mistakes and their reasons.
TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis
Specifically, we introduce a general-purpose TSPM that processes multi-scale time series using (1) multi-resolution time imaging (MRTI), (2) time image decomposition (TID), (3) multi-scale mixing (MCM), and (4) multi-resolution mixing (MRM) to extract comprehensive temporal patterns.
ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization
Multimodal Large Language Models (MLLMs), such as GPT4o, have shown strong capabilities in visual reasoning and explanation generation.
Pattern based learning and optimisation through pricing for bin packing problem
As a popular form of knowledge and experience, patterns and their identification have been critical tasks in most data mining applications.
Deep learning for predicting the occurrence of tipping points
The ability to predict the occurrence of tipping points from time series data remains an outstanding challenge and a major interest in a broad range of research fields.
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models
In this paper, we reveal that VLP models are also vulnerable to the instance-agnostic universal adversarial perturbation (UAP).
UniCL: A Universal Contrastive Learning Framework for Large Time Series Models
Time-series analysis plays a pivotal role across a range of critical applications, from finance to healthcare, which involves various tasks, such as forecasting and classification.
DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery
Satellite imagery has played an increasingly important role in post-disaster building damage assessment.
The Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0
We conclude that the BERT base model will be improved by incorporating the features.
Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusion
When trained using KNN on CIFAR10, MLOD-Fisher significantly lowers the false positive rate (FPR) from 24. 09% to 7. 47% on average compared to merely utilizing the features of the last layer.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
An Adaptive Dimension Reduction Estimation Method for High-dimensional Bayesian Optimization
Initially, we identify the effective dimension reduction (EDR) subspace for the objective function using the minimum average variance estimation (MAVE) method.
High-Quality Mesh Blendshape Generation from Face Videos via Neural Inverse Rendering
Experiments demonstrate that, with the flexible input of single or sparse multi-view videos, we reconstruct personalized high-fidelity blendshapes.
LiDAR-Net: A Real-scanned 3D Point Cloud Dataset for Indoor Scenes
In this paper we present LiDAR-Net a new real-scanned indoor point cloud dataset containing nearly 3. 6 billion precisely point-level annotated points covering an expansive area of 30000m^2.
PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer
Language style is necessary for AI systems to understand and generate diverse human language accurately.
MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence.
Automated Repair of Declarative Software Specifications in the Era of Large Language Models
In this study, we assess the effectiveness of utilizing OpenAI's ChatGPT to repair software specifications written in the Alloy declarative language.
Enhancing Signed Graph Neural Networks through Curriculum-Based Training
The prevailing training approach feeds samples (edges) to models in a random order, resulting in equal contributions from each sample during the training process, but fails to account for varying learning difficulties based on the graph's structure.
Adversarial Attacks on Combinatorial Multi-Armed Bandits
Contrary to prior understanding of multi-armed bandits, our work reveals a surprising fact that the attackability of a specific CMAB instance also depends on whether the bandit instance is known or unknown to the adversary.
ALIP: Adaptive Language-Image Pre-training with Synthetic Caption
However, the presence of intrinsic noise and unmatched image-text pairs in web data can potentially affect the performance of representation learning.
Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey
Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market.
Learning a Graph Neural Network with Cross Modality Interaction for Image Fusion
Finally, we merge all graph features to get the fusion result.
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning
This paper proposes DiffFit, a parameter-efficient strategy to fine-tune large pre-trained diffusion models that enable fast adaptation to new domains.
Efficient Diffusion Personalization
parameter-efficient fine-tuning
Fair-CDA: Continuous and Directional Augmentation for Group Fairness
By adjusting the perturbation strength in the direction of the paths, our proposed augmentation is controllable and auditable.
Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers
In this work, the VQVAE focus on feature extraction and reconstruction of images, and the transformers fit the manifold and locate anomalies in the latent space.
Bridging the Emotional Semantic Gap via Multimodal Relevance Estimation
To tackle this problem, we propose a multimodal relevance estimation network to capture the relevant semantics among modalities in multimodal emotions.
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model
Unsupervised surface anomaly detection aims at discovering and localizing anomalous patterns using only anomaly-free training samples.
Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain
Specifically, MSFDPM consists of a side information feature extractor, a multi-scale feature domain patch matching module, and a multi-scale feature fusion network.
SIAD: Self-supervised Image Anomaly Detection System
To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios.
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization
Few-shot abstractive summarization has become a challenging task in natural language generation.
Ask to Understand: Question Generation for Multi-hop Question Answering
Specifically, we carefully design an end-to-end QG module on the basis of a classical QA module, which could help the model understand the context by asking inherently logical sub-questions, thus inheriting interpretability from the QD-based method and showing superior performance.
Code Smells in Machine Learning Systems
We found that our newly identified code smells are prevalent and impactful on the maintenance of DL systems from the developer's perspective.
Dynamic Texture Recognition using PDV Hashing and Dictionary Learning on Multi-scale Volume Local Binary Pattern
To tackle this problem, we propose a method for dynamic texture recognition using PDV hashing and dictionary learning on multi-scale volume local binary pattern (PHD-MVLBP).
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation.
Salient Object Ranking with Position-Preserved Attention
In this paper, we study the Salient Object Ranking (SOR) task, which manages to assign a ranking order of each detected object according to its visual saliency.
Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework
Since the existing naive model parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication, and analogy multi-party learning as a many-to-one knowledge sharing problem.
Analytics and Machine Learning in Vehicle Routing Research
The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimisation problems for which numerous models and algorithms have been proposed.
Horizontal-to-Vertical Video Conversion
Alongside the prevalence of mobile videos, the general public leans towards consuming vertical videos on hand-held devices.
MetaAugment: Sample-Aware Data Augmentation Policy Learning
Automated data augmentation has shown superior performance in image recognition.
Amodal Segmentation Based on Visible Region Segmentation and Shape Prior
In this way, features corresponding to background and occlusion can be suppressed for amodal mask estimation.
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
JSRT: James-Stein Regression Tree
To address this issue, we propose a novel regression tree, named James-Stein Regression Tree (JSRT) by considering global information from different nodes.
Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning
Interpretability and effectiveness are two essential and indispensable requirements for adopting machine learning methods in reality.
HRINet: Alternative Supervision Network for High-resolution CT image Interpolation
We combine the idea of ACAI and GANs, and propose a novel idea of alternative supervision method by applying supervised and unsupervised training alternatively to raise the accuracy of human organ structures in CT while keeping high quality.
Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness
How to obtain a model with good interpretability and performance has always been an important research topic.
Multinomial Random Forest: Toward Consistency and Privacy-Preservation
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood.
Dynamic Label Graph Matching for Unsupervised Video Re-Identification
Label estimation is an important component in an unsupervised person re-identification (re-ID) system.
Ranked #8 on
Person Re-Identification
on PRID2011
