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Found 35 papers, 11 papers with code
Benchmarking Large Language Models on Communicative Medical Coaching: a Novel System and Dataset
To construct the ChatCoach system, we developed a dataset and integrated Large Language Models such as ChatGPT and Llama2, aiming to assess their effectiveness in communicative medical coaching tasks.
Advancing Test-Time Adaptation for Acoustic Foundation Models in Open-World Shifts
However, while acoustic models face similar challenges due to distribution shifts in test-time speech, TTA techniques specifically designed for acoustic modeling in the context of open-world data shifts remain scarce.
Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning
Large Language models (LLMs) possess the capability to engage In-context Learning (ICL) by leveraging a few demonstrations pertaining to a new downstream task as conditions.
Dual Node and Edge Fairness-Aware Graph Partition
To address this gap, we propose a notion edge balance to measure the proportion of edges connecting different demographic groups in clusters.
Zero-Shot Automatic Pronunciation Assessment
Automatic Pronunciation Assessment (APA) is vital for computer-assisted language learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Visualizing Transferred Knowledge: An Interpretive Model of Unsupervised Domain Adaptation
Many research efforts have been committed to unsupervised domain adaptation (DA) problems that transfer knowledge learned from a labeled source domain to an unlabeled target domain.
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery
Generalized Zero-Shot Learning (GZSL) and Open-Set Recognition (OSR) are two mainstream settings that greatly extend conventional visual object recognition.
Learning Antidote Data to Individual Unfairness
Fairness is essential for machine learning systems deployed in high-stake applications.
Characterizing the Influence of Graph Elements
To fill this gap, we started with the simple graph convolution (SGC) model that operates on an attributed graph and formulated an influence function to approximate the changes in model parameters when a node or an edge is removed from an attributed graph.
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework
Experimentally, we observe that CFC is highly robust to the proposed attack and is thus a truly robust fair clustering alternative.
Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings
In this paper, we consider a novel research problem: music-to-text synaesthesia.
Contrastive Graph Few-Shot Learning
Later, the trained encoder is frozen as a teacher model to distill a student model with a contrastive loss.
Multi-task Envisioning Transformer-based Autoencoder for Corporate Credit Rating Migration Early Prediction
META consists of Positional Encoding, Transformer-based Autoencoder, and Multi-task Prediction to learn effective representations for both migration prediction and rating prediction.
Learnable Visual Words for Interpretable Image Recognition
In this paper, we revisit the concept of visual words and propose the Learnable Visual Words (LVW) to interpret the model prediction behaviors with two novel modules: semantic visual words learning and dual fidelity preservation.
Label-invariant Augmentation for Semi-Supervised Graph Classification
Recently, contrastiveness-based augmentation surges a new climax in the computer vision domain, where some operations, including rotation, crop, and flip, combined with dedicated algorithms, dramatically increase the model generalization and robustness.
Exploiting Temporal Relations on Radar Perception for Autonomous Driving
We consider the object recognition problem in autonomous driving using automotive radar sensors.
Ranked #1 on
Multiple Object Tracking
on RADIATE
Achieving Fairness at No Utility Cost via Data Reweighing with Influence
With the fast development of algorithmic governance, fairness has become a compulsory property for machine learning models to suppress unintentional discrimination.
Implicit Semantic Response Alignment for Partial Domain Adaptation
Partial Domain Adaptation (PDA) addresses the unsupervised domain adaptation problem where the target label space is a subset of the source label space.
Second-Order Unsupervised Feature Selection via Knowledge Contrastive Distillation
Unsupervised feature selection aims to select a subset from the original features that are most useful for the downstream tasks without external guidance information.
Code Editing from Few Exemplars by Adaptive Multi-Extent Composition
To achieve this, we propose a machine learning approach to adapt the editorial style derived from few exemplars to a query code snippet.
IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation
By employing score-based outlier detectors for initialization, iPOF updates each data point's outlier score by averaging the outlier factors of its nearest common neighbors.
Economic Recession Prediction Using Deep Neural Network
We investigate the effectiveness of different machine learning methodologies in predicting economic cycles.
STRODE: Stochastic Boundary Ordinary Differential Equation
In this paper, we present a probabilistic ordinary differential equation (ODE), called STochastic boundaRy ODE (STRODE), that learns both the timings and the dynamics of time series data without requiring any timing annotations during training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Deep Clustering based Fair Outlier Detection
In this paper, we focus on the fairness issues regarding unsupervised outlier detection.
SelfDoc: Self-Supervised Document Representation Learning
For downstream usage, we propose a novel modality-adaptive attention mechanism for multimodal feature fusion by adaptively emphasizing language and vision signals.
Fairness-Aware Unsupervised Feature Selection
Feature selection is a prevalent data preprocessing paradigm for various learning tasks.
Towards Novel Target Discovery Through Open-Set Domain Adaptation
In this paper, we propose a novel framework to accurately identify the seen categories in target domain, and effectively recover the semantic attributes for unseen categories.
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
Disparate impact has raised serious concerns in machine learning applications and its societal impacts.
Graph-Graph Similarity Network
In this paper, we propose a Graph-Graph Similarity Network to tackle the graph classification problem by constructing a SuperGraph through learning the relationships among graphs.
Mining Label Distribution Drift in Unsupervised Domain Adaptation
Unsupervised domain adaptation targets to transfer task-related knowledge from labeled source domain to unlabeled target domain.
Learnable Subspace Clustering
This paper studies the large-scale subspace clustering (LSSC) problem with million data points.
Consensus Clustering: An Embedding Perspective, Extension and Beyond
Consensus clustering fuses diverse basic partitions (i. e., clustering results obtained from conventional clustering methods) into an integrated one, which has attracted increasing attention in both academic and industrial areas due to its robust and effective performance.
Spatially Constrained GAN for Face and Fashion Synthesis
Besides, a segmentor network is constructed to impose spatial constraints on the generator.
Predictive Local Smoothness for Stochastic Gradient Methods
To address this problem, we propose a simple yet effective method for improving stochastic gradient methods named predictive local smoothness (PLS).
Clustering with Outlier Removal
Then an objective function based Holoentropy is designed to enhance the compactness of each cluster with a few outliers removed.
