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Found 50 papers, 18 papers with code
Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives
To bring clarity to the current research status and identify opportunities for future work, we conducted a comprehensive review of $88$ papers: a systematic literature review of $18$ papers focused on LLMs in SE, complemented by an analysis of 70 papers from broader trust literature.
EmoDiffusion: Enhancing Emotional 3D Facial Animation with Latent Diffusion Models
Speech-driven 3D facial animation seeks to produce lifelike facial expressions that are synchronized with the speech content and its emotional nuances, finding applications in various multimedia fields.
RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading Premises
Recent advances in large language models (LLMs) have shown that they can answer questions requiring complex reasoning.
Language-TPP: Integrating Temporal Point Processes with Language Models for Event Analysis
Temporal Point Processes (TPPs) have been widely used for event sequence modeling, but they often struggle to incorporate rich textual event descriptions effectively.
A computational loudness model for electrical stimulation with cochlear implants
A computational model that uses a three-dimensional (3D) representation of the peripheral auditory system of CI users was developed to predict categorical loudness from the simulated peripheral neural activity.
Advances in Temporal Point Processes: Bayesian, Deep, and LLM Approaches
Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time.
Hierarchical Multi-Graphs Learning for Robust Group Re-Identification
Group Re-identification (G-ReID) faces greater complexity than individual Re-identification (ReID) due to challenges like mutual occlusion, dynamic member interactions, and evolving group structures.
Navigating Towards Fairness with Data Selection
Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fairness.
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression
This work addresses a key limitation in current federated learning approaches, which predominantly focus on homogeneous tasks, neglecting the task diversity on local devices.
OOD-HOI: Text-Driven 3D Whole-Body Human-Object Interactions Generation Beyond Training Domains
Generating realistic 3D human-object interactions (HOIs) from text descriptions is a active research topic with potential applications in virtual and augmented reality, robotics, and animation.
Causal Representation Learning from Multimodal Biomedical Observations
Theoretically, we consider a nonparametric latent distribution (c. f., parametric assumptions in previous work) that allows for causal relationships across potentially different modalities.
Nonstationary Sparse Spectral Permanental Process
Existing permanental processes often impose constraints on kernel types or stationarity, limiting the model's expressiveness.
The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize
By leveraging the smoothness and recurrence properties of the SA updates, we develop a fine-grained analysis of the correlation between the SA iterates $\theta_k$ and Markovian data $x_k$.
MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Education
Mathematical modeling (MM) is considered a fundamental skill for students in STEM disciplines.
Prelimit Coupling and Steady-State Convergence of Constant-stepsize Nonsmooth Contractive SA
Motivated by Q-learning, we study nonsmooth contractive stochastic approximation (SA) with constant stepsize.
Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed.
Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency
Fine-tuning pre-trained models is a widely employed technique in numerous real-world applications.
Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models
We have experimentally observed that LLMs possess the capability to understand the "confidence" in their own responses.
Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents
However, LLMs are optimized for language generation instead of tool use during training or alignment, limiting their effectiveness as agents.
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Deep equilibrium models (DEQs), as a typical implicit neural network, have demonstrated remarkable success on various tasks.
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation
By connecting the constant stepsize Q-learning to a time-homogeneous Markov chain, we show the distributional convergence of the iterates in Wasserstein distance and establish its exponential convergence rate.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Dynamic multi-graph neural network for traffic flow prediction incorporating traffic accidents
Second, we designed a dynamic graph adjustment module to update the adjacency matrix used in each training step.
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it.
Mitigating Label Bias in Machine Learning: Fairness through Confident Learning
In this paper, we demonstrate that despite only having access to the biased labels, it is possible to eliminate bias by filtering the fairest instances within the framework of confident learning.
Leveraging Laryngograph Data for Robust Voicing Detection in Speech
Accurately detecting voiced intervals in speech signals is a critical step in pitch tracking and has numerous applications.
Distilling the Unknown to Unveil Certainty
This paper introduces OOD knowledge distillation, a pioneering learning framework applicable whether or not training ID data is available, given a standard network.
CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents
We hope that the framework and environment can be a promising testbed to study competition that fosters understanding of society.
Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE
To bridge this gap, we present ACLUE, an evaluation benchmark designed to assess the capability of language models in comprehending ancient Chinese.
MetaAgents: Simulating Interactions of Human Behaviors for LLM-based Task-oriented Coordination via Collaborative Generative Agents
We propose a novel framework that equips collaborative generative agents with human-like reasoning abilities and specialized skills.
Advancing Acoustic Howling Suppression through Recursive Training of Neural Networks
In this paper, we introduce a novel training framework designed to comprehensively address the acoustic howling issue by examining its fundamental formation process.
Neural Network Augmented Kalman Filter for Robust Acoustic Howling Suppression
Acoustic howling suppression (AHS) is a critical challenge in audio communication systems.
Unifying Robustness and Fidelity: A Comprehensive Study of Pretrained Generative Methods for Speech Enhancement in Adverse Conditions
Enhancing speech signal quality in adverse acoustic environments is a persistent challenge in speech processing.
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts.
CMMLU: Measuring massive multitask language understanding in Chinese
As the capabilities of large language models (LLMs) continue to advance, evaluating their performance becomes increasingly crucial and challenging.
Hierarchical Optimization-Derived Learning
In recent years, by utilizing optimization techniques to formulate the propagation of deep model, a variety of so-called Optimization-Derived Learning (ODL) approaches have been proposed to address diverse learning and vision tasks.
NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation
The robustness of the Kalman filter to double talk and its rapid convergence make it a popular approach for addressing acoustic echo cancellation (AEC) challenges.
De-biased Representation Learning for Fairness with Unreliable Labels
In other words, the fair pre-processing methods ignore the discrimination encoded in the labels either during the learning procedure or the evaluation stage.
Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training
Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization.
Continuous Speech Separation with Recurrent Selective Attention Network
In this paper, we propose to apply recurrent selective attention network (RSAN) to CSS, which generates a variable number of output channels based on active speaker counting.
Value-Function-based Sequential Minimization for Bi-level Optimization
We also extend BVFSM to address BLO with additional functional constraints.
Bias-Tolerant Fair Classification
Therefore, we propose a Bias-TolerantFAirRegularizedLoss (B-FARL), which tries to regain the benefits using data affected by label bias and selection bias.
Nonlinear Hawkes Processes in Time-Varying System
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena.
Efficient Inference of Flexible Interaction in Spiking-neuron Networks
Hawkes process provides an effective statistical framework for analyzing the time-dependent interaction of neuronal spiking activities.
Digital Collaborator: Augmenting Task Abstraction in Visualization Design with Artificial Intelligence
In the task abstraction phase of the visualization design process, including in "design studies", a practitioner maps the observed domain goals to generalizable abstract tasks using visualization theory in order to better understand and address the users needs.
Fine-grained Image-to-Image Transformation towards Visual Recognition
In this paper, we aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image, which can thereby benefit the subsequent fine-grained image recognition and few-shot learning tasks.
Fashion Editing with Adversarial Parsing Learning
Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value.
Human-Centered Emotion Recognition in Animated GIFs
The framework consists of a facial attention module and a hierarchical segment temporal module.
End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception
In this work, we propose a multi-task learning framework to predict the steering angle and speed control simultaneously in an end-to-end manner.
Boundary-based Image Forgery Detection by Fast Shallow CNN
In this paper, we substantiate that Fast SCNN can detect drastic change of chroma and saturation.
