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Found 17 papers, 5 papers with code
Will the Inclusion of Generated Data Amplify Bias Across Generations in Future Image Classification Models?
As the demand for high-quality training data escalates, researchers have increasingly turned to generative models to create synthetic data, addressing data scarcity and enabling continuous model improvement.
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
In this work, we propose a data-centric framework, Biomedical Visual Instruction Tuning with Clinician Preference Alignment (BioMed-VITAL), that incorporates clinician preferences into both stages of generating and selecting instruction data for tuning biomedical multimodal foundation models.
CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models
A naive method is to simply increase the masking ratio and the likelihood of masking attack tokens, but it leads to a significant reduction in both certified accuracy and the certified radius due to input corruption by extensive masking.
FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation
Preference-based learning aims to align robot task objectives with human values.
OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery
Existing algorithms dedicated to surgical activity recognition predominantly cater to pre-defined closed-set paradigms, ignoring the challenges of real-world open-set scenarios.
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids.
Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysis
Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor.
AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning
By integrating Multi-Agent Reinforcement Learning, our framework ensures that participatory urban planning decisions are more dynamic and adaptive to evolving community needs and provides a robust platform for automating complex real-world urban planning processes.
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks
The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data.
Semi-Supervised Clustering via Dynamic Graph Structure Learning
However, existing methods adopt a static affinity matrix to learn the low-dimensional representations of data points and do not optimize the affinity matrix during the learning process.
Multi-task Deep Neural Networks for Massive MIMO CSI Feedback
Deep learning has been widely applied for the channel state information (CSI) feedback in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system.
Nonlinear Kalman Filter Using Cramer Rao Bound
This paper studies the optimal state estimation for a dynamic system, whose transfer function can be nonlinear and the input noise can be of arbitrary distribution.
Exploring Autoencoder-based Error-bounded Compression for Scientific Data
(1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model.
On the Optimality of the Oja's Algorithm for Online PCA
In this paper we analyze the behavior of the Oja's algorithm for online/streaming principal component subspace estimation.
cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data
To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data.
Distributed, Parallel, and Cluster Computing
FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks
(1) We propose several systematic ABFT schemes based on checksum techniques and analyze their fault protection ability and runtime thoroughly. Unlike traditional ABFT based on matrix-matrix multiplication, our schemes support any convolution implementations.
DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression
In this paper, we propose DeepSZ: an accuracy-loss bounded neural network compression framework, which involves four key steps: network pruning, error bound assessment, optimization for error bound configuration, and compressed model generation, featuring a high compression ratio and low encoding time.
