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Found 33 papers, 15 papers with code
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
Asymmetry in Low-Rank Adapters of Foundation Models
Specifically, when updating the parameter matrices of a neural network by adding a product $BA$, we observe that the $B$ and $A$ matrices have distinct functions: $A$ extracts features from the input, while $B$ uses these features to create the desired output.
Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge
The multi-scale uncertainty analysis is obtained by using the distribution characteristics of global and local latent variables.
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving
However, maintaining safety in diverse safety-critical scenarios remains a significant challenge due to long-tailed and unforeseen scenarios absent from offline datasets.
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations
The current certification process for assessing robustness is costly and time-consuming due to the extensive number of image projections required for Monte Carlo sampling in the 3D camera motion space.
Datasets and Benchmarks for Offline Safe Reinforcement Learning
This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the training and deployment phases.
MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos
To address these challenges and provide a comprehensive dataset for this new direction, we have meticulously curated the \textbf{MMSum} dataset.
Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging
Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare.
Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report
Automated interpretation of electrocardiograms (ECG) has garnered significant attention with the advancements in machine learning methodologies.
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning
In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning.
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics
Our work provides a new perspective of model robustness through the lens of Wasserstein geodesic-based interpolation with a practical off-the-shelf strategy that can be combined with existing robust training methods.
Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
The learned embeddings are evaluated on two downstream tasks: (1) automatic ECG diagnosis report generation, and (2) zero-shot cardiovascular disease detection.
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks.
Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment
Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding.
Robustness Certification of Visual Perception Models via Camera Motion Smoothing
To this end, we study the robustness of the visual perception model under camera motion perturbations to investigate the influence of camera motion on robotic perception.
Can Brain Signals Reveal Inner Alignment with Human Languages?
In addition, we provide interpretations of the performance improvement: (1) feature distribution shows the effectiveness of the alignment module for discovering and encoding the relationship between EEG and language; (2) alignment weights show the influence of different language semantics as well as EEG frequency features; (3) brain topographical maps provide an intuitive demonstration of the connectivity in the brain regions.
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction
In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals.
MHMS: Multimodal Hierarchical Multimedia Summarization
Multimedia summarization with multimodal output can play an essential role in real-world applications, i. e., automatically generating cover images and titles for news articles or providing introductions to online videos.
PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression
The proposed method learns an individual-specific predictive model from heterogeneous observations, and enables estimation of an optimal transport map that yields a push forward operation onto the demographic features for each task.
Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation
In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection.
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations.
Functional optimal transport: map estimation and domain adaptation for functional data
We introduce a formulation of optimal transport problem for distributions on function spaces, where the stochastic map between functional domains can be partially represented in terms of an (infinite-dimensional) Hilbert-Schmidt operator mapping a Hilbert space of functions to another.
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
The algorithm is evaluated in realistic safety-critical environments with non-stationary disturbances.
SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments
Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving, and the generalization of learning-based algorithms on different environments is still an open problem.
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
We propose a transition prior to account for the temporal dependencies in streaming data and update the mixture online via sequential variational inference.
Robust Unsupervised Learning of Temporal Dynamic Interactions
These distance metrics can serve as an objective for assessing the stability of an interaction learning algorithm.
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
Results demonstrate that our proposed Bayesian nonparametric approach provides an insight into the complicated lane-change interactions of the ego vehicle with multiple surrounding traffic participants based on the interpretable interaction patterns and their transition properties in temporal relationships.
Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process
Constructed by incorporating NPs with recurrent neural networks (RNNs), the ARNP model predicts the distribution of a target vehicle trajectory conditioned on the observed long-term sequential data of all surrounding vehicles.
Recurrent Attentive Neural Process for Sequential Data
Neural processes (NPs) learn stochastic processes and predict the distribution of target output adaptively conditioned on a context set of observed input-output pairs.
A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos
Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions.
Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field
We then use this model to reproduce the high-dimensional driving scenarios in a finitely tractable form.
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives
A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV).
