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Found 19 papers, 10 papers with code
Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification
Unsupervised image classification is a challenging computer vision task.
Ranked #1 on
Image Clustering
on STL-10
(Backbone metric, using extra
training data)
Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus
The existing methods for evaluating the inference abilities of Large Language Models (LLMs) have been results-centric, making it difficult to assess the inference process.
Explainable Product Classification for Customs
The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices.
Towards Attack-tolerant Federated Learning via Critical Parameter Analysis
Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other.
FedDefender: Client-Side Attack-Tolerant Federated Learning
Evaluations of real-world scenarios across multiple datasets show that the proposed method enhances the robustness of federated learning against model poisoning attacks.
Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformer
In the pursuit of artificial general intelligence (AGI), we tackle Abstraction and Reasoning Corpus (ARC) tasks using a novel two-pronged approach.
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications.
Customs Import Declaration Datasets
Given the huge volume of cross-border flows, effective and efficient control of trade becomes more crucial in protecting people and society from illicit trade.
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
This paper presents FedX, an unsupervised federated learning framework.
Knowledge Sharing via Domain Adaptation in Customs Fraud Detection
Knowledge of the changing traffic is critical in risk management.
Classification of Goods Using Text Descriptions With Sentences Retrieval
The task of assigning and validating internationally accepted commodity code (HS code) to traded goods is one of the critical functions at the customs office.
Coherence-based Label Propagation over Time Series for Accelerated Active Learning
Time-series data are ubiquitous these days, but lack of the labels in time-series data is regarded as a hurdle for its broad applicability.
Customs Fraud Detection in the Presence of Concept Drift
Capturing the changing trade pattern is critical in customs fraud detection.
Improving Unsupervised Image Clustering With Robust Learning
Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results.
Ranked #1 on
Image Clustering
on CIFAR-100
(Train Set metric, using extra
training data)
Active Learning for Human-in-the-Loop Customs Inspection
We study the human-in-the-loop customs inspection scenario, where an AI-assisted algorithm supports customs officers by recommending a set of imported goods to be inspected.
DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection
Intentional manipulation of invoices that lead to undervaluation of trade goods is the most common type of customs fraud to avoid ad valorem duties and taxes.
Carpe Diem, Seize the Samples Uncertain "At the Moment" for Adaptive Batch Selection
Compared with existing batch selection methods, the results showed that Recency Bias reduced the test error by up to 20. 97% in a fixed wall-clock training time.
Ada-Boundary: Accelerating the DNN Training via Adaptive Boundary Batch Selection
Neural networks can converge faster with help from a smarter batch selection strategy.
Automatic Knowledge Base Evolution by Learning Instances
Knowledge base is the way to store structured and unstructured data throughout the web.
