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Found 43 papers, 14 papers with code
DiverseAgentEntropy: Quantifying Black-Box LLM Uncertainty through Diverse Perspectives and Multi-Agent Interaction
In this paper, we propose a novel method, DiverseAgentEntropy, for evaluating a model's uncertainty using multi-agent interaction under the assumption that if a model is certain, it should consistently recall the answer to the original query across a diverse collection of questions about the same original query.
Open Domain Question Answering with Conflicting Contexts
Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions.
Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering
This demonstrates the potential of optical flow data, combined with advanced neural network architectures (a CNN-based structure for fusing data and a Recurrence-based network for inferring a command from latent space), to enhance the performance of autonomous vehicles steering estimation.
Human Insights Driven Latent Space for Different Driving Perspectives: A Unified Encoder for Efficient Multi-Task Inference
While our performance in steering angle estimation is comparable to existing methods, the integration of human-like perception through multi-task learning holds significant potential for advancing autonomous driving systems.
ABNet: Attention BarrierNet for Safe and Scalable Robot Learning
Safe learning is central to AI-enabled robots where a single failure may lead to catastrophic results.
Secure Control of Connected and Automated Vehicles Using Trust-Aware Robust Event-Triggered Control Barrier Functions
We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to safely navigate through a conflict area (e. g., traffic intersections, merging roadways, roundabouts).
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models
As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning.
Safe Optimal Interactions Between Automated and Human-Driven Vehicles in Mixed Traffic with Event-triggered Control Barrier Functions
This paper studies safe driving interactions between Human-Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) in mixed traffic where the dynamics and control policies of HDVs are unknown and hard to predict.
Safe Neural Control for Non-Affine Control Systems with Differentiable Control Barrier Functions
To address these challenges, we incorporate higher-order CBFs into neural ordinary differential equation-based learning models as differentiable CBFs to guarantee safety for non-affine control systems.
Successive one-sided Hodrick-Prescott filter with incremental filtering algorithm for nonlinear economic time series
We propose a successive one-sided Hodrick-Prescott (SOHP) filter from multiple time scale decomposition perspective to derive trend estimate for a time series.
SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees, thus making it hard to be applied for safety-critical applications.
Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles
We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area.
Learning Robust and Correct Controllers from Signal Temporal Logic Specifications Using BarrierNet
In this paper, we consider the problem of learning a neural network controller for a system required to satisfy a Signal Temporal Logic (STL) specification.
Learning Stability Attention in Vision-based End-to-end Driving Policies
Modern end-to-end learning systems can learn to explicitly infer control from perception.
Learning Feasibility Constraints for Control Barrier Functions
It has been shown that optimizing quadratic costs while stabilizing affine control systems to desired (sets of) states subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs).
Learned Risk Metric Maps for Kinodynamic Systems
We present Learned Risk Metric Maps (LRMM) for real-time estimation of coherent risk metrics of high dimensional dynamical systems operating in unstructured, partially observed environments.
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization
Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries.
Interpreting Neural Policies with Disentangled Tree Representations
The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning.
On the Forward Invariance of Neural ODEs
We propose a new method to ensure neural ordinary differential equations (ODEs) satisfy output specifications by using invariance set propagation.
Optimal Control of Connected Automated Vehicles with Event/Self-Triggered Control Barrier Functions
We address the problem of controlling Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network subject to hard safety constraints.
Learning Dialogue Representations from Consecutive Utterances
In this paper, we introduce Dialogue Sentence Embedding (DSE), a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue tasks.
Self-Triggered Coordination Control of Connected Automated Vehicles in Traffic Networks
The safety constraints and the vehicle limitations are considered using the Control Barrier Function (CBF) framework and a self-triggered scheme is proposed using the CBF constraints.
Optimal Control of Connected Automated Vehicles with Event-Triggered Control Barrier Functions
We address the problem of controlling Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network subject to hard safety constraints.
Feasibility Guaranteed Traffic Merging Control Using Control Barrier Functions
We consider the merging control problem for Connected and Automated Vehicles (CAVs) aiming to jointly minimize travel time and energy consumption while providing speed-dependent safety guarantees and satisfying velocity and acceleration constraints.
Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving
They are interpretable at scale, achieve great test performance under limited training data, and are safety guaranteed in a series of autonomous driving scenarios such as lane keeping and obstacle avoidance.
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency.
BarrierNet: A Safety-Guaranteed Layer for Neural Networks
This paper introduces differentiable higher-order control barrier functions (CBF) that are end-to-end trainable together with learning systems.
Two-stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant
Intelligent personal assistants (IPA) enable voice applications that facilitate people's daily tasks.
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora
We evaluate PTLM's ability to adapt to new corpora while retaining learned knowledge in earlier corpora.
Virtual Augmentation Supported Contrastive Learning of Sentence Representations
We then define an instance discrimination task regarding this neighborhood and generate the virtual augmentation in an adversarial training manner.
Pairwise Supervised Contrastive Learning of Sentence Representations
Many recent successes in sentence representation learning have been achieved by simply fine-tuning on the Natural Language Inference (NLI) datasets with triplet loss or siamese loss.
Decentralized Time and Energy-Optimal Control of Connected and Automated Vehicles in a Roundabout
The paper considers the problem of controlling Connected and Automated Vehicles (CAVs) traveling through a three-entry roundabout so as to jointly minimize both the travel time and the energy consumption while providing speed-dependent safety guarantees, as well as satisfying velocity and acceleration constraints.
Event-Triggered Safety-Critical Control for Systems with Unknown Dynamics
We define a HOCBF for a safety requirement on the unmodelled system based on the adaptive dynamics and error states, and reformulate the safety-critical control problem as the above mentioned QP.
Rule-based Optimal Control for Autonomous Driving
We develop optimal control strategies for Autonomous Vehicles (AVs) that are required to meet complex specifications imposed by traffic laws and cultural expectations of reasonable driving behavior.
Autonomous Driving
Robotics
Systems and Control
Systems and Control
When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition with Limited Data
Then the activated dictionary atoms are assembled and passed to the compound dictionary learning and coding layers.
Feasibility-Guided Learning for Robust Control in Constrained Optimal Control Problems
In this paper, we further improve the feasibility robustness (i. e., feasibility maintenance in the presence of time-varying and unknown unsafe sets) through the definition of a High Order CBF (HOCBF) that works for arbitrary relative degree constraints; this is achieved by a proposed feasibility-guided learning approach.
Fast and Robust Dynamic Hand Gesture Recognition via Key Frames Extraction and Feature Fusion
Gesture recognition is a hot topic in computer vision and pattern recognition, which plays a vitally important role in natural human-computer interface.
Ranked #1 on
Hand Gesture Recognition
on Cambridge
Deep Micro-Dictionary Learning and Coding Network
In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN).
An Online Algorithm for Nonparametric Correlations
This paper investigates the problem of computing nonparametric correlations on the fly for streaming data.
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification
The ability to automatically and accurately process customer feedback is a necessity in the private sector.
Online Robust Principal Component Analysis with Change Point Detection
Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing.
Sampling Method for Fast Training of Support Vector Data Description
Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data.
A Probabilistic Machine Learning Approach to Detect Industrial Plant Faults
Fault detection in industrial plants is a hot research area as more and more sensor data are being collected throughout the industrial process.
