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Found 26 papers, 6 papers with code
Dense Vision Transformer Compression with Few Samples
In particular, the issue of sparse compression exists in traditional CNN few-shot methods, which can only produce very few compressed models of different model sizes.
Confidence Self-Calibration for Multi-Label Class-Incremental Learning
The partial label challenge in Multi-Label Class-Incremental Learning (MLCIL) arises when only the new classes are labeled during training, while past and future labels remain unavailable.
FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation
In this paper, we introduce FRESCO, intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint.
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation
However, simply adopting models derived from DFKD for real-world applications suffers significant performance degradation, due to the discrepancy between teachers' training data and real-world scenarios (student domain).
BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling
Recent efforts in using 3D Gaussians for scene reconstruction and novel view synthesis can achieve impressive results on curated benchmarks; however, images captured in real life are often blurry.
Quantum Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability
By training and employing a machine learning model that identifies and corrects the noise in quantum processed images, we can compensate for the noisiness caused by the machine and retrieve a processing result similar to that performed by a classical computer with higher efficiency.
"Task Success" is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors
Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences.
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction
This paper presents a unique solution to challenges in medical image processing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation.
DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing
Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively, and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition, where correspondence automatically emerges without the need for annotation.
Self-Supervised Learning of Whole and Component-Based Semantic Representations for Person Re-Identification
To address these limitations, we propose a Local Semantic Extraction (LSE) module inspired by Interactive Segmentation Models.
Software-Defined Virtual Synchronous Condenser
Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids.
Physics-Informed Induction Machine Modelling
This rapid communication devises a Neural Induction Machine (NeuIM) model, which pilots the use of physics-informed machine learning to enable AI-based electromagnetic transient simulations.
Physics-Aware Neural Dynamic Equivalence of Power Systems
The contributions are threefold: (1) an ODE-Net-enabled NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems; (2) a physics-informed NeuDyE learning method (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE without an additional verification module; (3) a physics-guided NeuDyE (PG-NeuDyE) to enhance the method's applicability even in the absence of analytical physics models.
Scalable Neural Dynamic Equivalence for Power Systems
Traditional grid analytics are model-based, relying strongly on accurate models of power systems, especially the dynamic models of generators, controllers, loads and other dynamic components.
Implementation of The Future of Drug Discovery: QuantumBased Machine Learning Simulation (QMLS)
This paper is based on our first paper, where we pitched the concept of machine learning combined with quantum simulations.
Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation
The framework includes two parts: key frame translation and full video translation.
Neuro-Dynamic State Estimation for Networked Microgrids
We devise neuro-dynamic state estimation (Neuro-DSE), a learning-based dynamic state estimation (DSE) algorithm for networked microgrids (NMs) under unknown subsystems.
PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding
PanoFlow achieves state-of-the-art performance on the public OmniFlowNet and the established FlowScape benchmarks.
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving
In this paper, we propose a new deep network architecture for optical flow estimation in autonomous driving--CSFlow, which consists of two novel modules: Cross Strip Correlation module (CSC) and Correlation Regression Initialization module (CRI).
Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews
For the study of interpretability, we consider the attention weights distribution of single sentence and the attention weights of main aspect terms.
Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems
Transient stability assessment (TSA) is a cornerstone for resilient operations of today's interconnected power grids.
Friends and Foes in Learning from Noisy Labels
Learning from examples with noisy labels has attracted increasing attention recently.
Neuro-Reachability of Networked Microgrids
A neural ordinary differential equations network (ODE-Net)-enabled reachability method (Neuro-Reachability) is devised for the dynamic verification of networked microgrids (NMs) with unidentified subsystems and heterogeneous uncertainties.
Indications for very high metallicity and absence of methane for the eccentric exo-Saturn WASP-117b
Using a 1D atmosphere model with isothermal temperature, uniform cloud deck and equilibrium chemistry, the Bayesian evidence of a retrieval analysis of the transmission spectrum indicates a preference for a high atmospheric metallicity ${\rm [Fe/H]}=2. 58^{+0. 26}_{-0. 37}$ and clear skies.
Earth and Planetary Astrophysics Solar and Stellar Astrophysics
Reliability Validation of Learning Enabled Vehicle Tracking
However, it is unclear if and how the adversarial examples over learning components can affect the overall system-level reliability.
Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)
This paper proposes an approach to detect moving objects in Wide Area Motion Imagery (WAMI), in which the objects are both small and well separated.
