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Found 21 papers, 5 papers with code
Causal Graph Guided Steering of LLM Values via Prompts and Sparse Autoencoders
As large language models (LLMs) become increasingly integrated into critical applications, aligning their behavior with human values presents significant challenges.
Preference Discerning with LLM-Enhanced Generative Retrieval
We assess current state-of-the-art methods using our benchmark and show that they struggle to accurately discern user preferences.
Unifying Generative and Dense Retrieval for Sequential Recommendation
This hybrid approach provides insights into the trade-offs between these approaches and demonstrates improvements in efficiency and effectiveness for recommendation systems in small-scale benchmarks.
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making
As agents progress, the environment adaptively generates new tasks with social structures for agents to undertake.
MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System
In industrial recommendation systems, multi-task learning (learning multiple tasks simultaneously on a single model) is a predominant approach to save training/serving resources and improve recommendation performance via knowledge transfer between the joint learning tasks.
Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games
LASE allocates a portion of its rewards to co-players as gifts, with this allocation adapting dynamically based on the social relationship -- a metric evaluating the friendliness of co-players estimated by counterfactual reasoning.
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning
To address these issues, we propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm that enables few-shot adaptation to unseen policies in mixed-motive environments.
Map Optical Properties to Subwavelength Structures Directly via a Diffusion Model
The inverse design methods proposed for these subwavelength structures are vital to the development of new photonic devices.
EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy
Lung cancer is a devastating disease with the highest mortality rate among cancer types.
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization
We investigated uncertainty sampling, annotation redundancy restriction, and initial dataset selection techniques.
Deep-learning-based on-chip rapid spectral imaging with high spatial resolution
Spectral imaging extends the concept of traditional color cameras to capture images across multiple spectral channels and has broad application prospects.
MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks
Specifically, in each training iteration and adaptively for each part of the network, the gradient of an auxiliary loss is carefully reduced or enlarged to have a closer magnitude to the gradient of the target loss, preventing auxiliary tasks from being so strong that dominate the target task or too weak to help the target task.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department.
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable.
MixModule: Mixed CNN Kernel Module for Medical Image Segmentation
Convolutional neural networks (CNNs) have been successfully applied to medical image classification, segmentation, and related tasks.
Prostate Segmentation from 3D MRI Using a Two-Stage Model and Variable-Input Based Uncertainty Measure
Using PROMISE-12 data, we demonstrated the robustness of the two-stage model and showed high correlation of the proposed variable-input based uncertainty measures with GT-based performance.
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features
In this study we propose to use an ensemble of 3D U-Nets with different hyper-parameters for brain tumor segmentation.
A Self-Adaptive Network For Multiple Sclerosis Lesion Segmentation From Multi-Contrast MRI With Various Imaging Protocols
Furthermore, we showed our network achieved the maximal possible performance when one sequence is unavailable during deployment by comparing with separate networks trained on the corresponding input MRI sequences.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
