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Found 18 papers, 13 papers with code
PCDCNet: A Surrogate Model for Air Quality Forecasting with Physical-Chemical Dynamics and Constraints
Air quality forecasting (AQF) is critical for public health and environmental management, yet remains challenging due to the complex interplay of emissions, meteorology, and chemical transformations.
FoCTTA: Low-Memory Continual Test-Time Adaptation with Focus
Second, updating all BN layers requires storing the activations of all BN layers for backpropagation, exacerbating the memory demand.
FocusDD: Real-World Scene Infusion for Robust Dataset Distillation
To further improve the generalization of the distilled dataset, each synthesized image is augmented with a downsampled view of the original image.
Interpretable deep learning illuminates multiple structures fluorescence imaging: a path toward trustworthy artificial intelligence in microscopy
Live-cell imaging of multiple subcellular structures is essential for understanding subcellular dynamics.
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?
We seek strategies for training on the SIMPLE version of the tasks that improve performance on the corresponding HARD task, i. e., S2H generalization.
ESP-PCT: Enhanced VR Semantic Performance through Efficient Compression of Temporal and Spatial Redundancies in Point Cloud Transformers
Notably, ESP-PCT achieves a remarkable accuracy of 93. 2% while reducing the computational requirements (FLOPs) by 76. 9% and memory usage by 78. 2% compared to the existing Point Transformer model simultaneously.
LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition
To address this, we present the Localization and Focus Vision Transformer (LF-ViT).
MultiZoo & MultiBench: A Standardized Toolkit for Multimodal Deep Learning
Learning multimodal representations involves integrating information from multiple heterogeneous sources of data.
Multimodal Fusion Interactions: A Study of Human and Automatic Quantification
In order to perform multimodal fusion of heterogeneous signals, we need to understand their interactions: how each modality individually provides information useful for a task and how this information changes in the presence of other modalities.
Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications
In many machine learning systems that jointly learn from multiple modalities, a core research question is to understand the nature of multimodal interactions: how modalities combine to provide new task-relevant information that was not present in either alone.
CamDiff: Camouflage Image Augmentation via Diffusion Model
Specifically, we leverage the latent diffusion model to synthesize salient objects in camouflaged scenes, while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training (CLIP) model to prevent synthesis failures and ensure the synthesized object aligns with the input prompt.
Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework
The recent explosion of interest in multimodal applications has resulted in a wide selection of datasets and methods for representing and integrating information from different modalities.
Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions
Previous GCNs-based methods usually require providing spatial correlation graph structure of observation sites in advance.
MultiBench: Multiscale Benchmarks for Multimodal Representation Learning
In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiBench, a systematic and unified large-scale benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas.
AKG: Automatic Kernel Generation for Neural Processing Units using Polyhedral Transformations
Existing tensor compilers have proven their effectiveness in deploying deep neural networks on general-purpose hardware like CPU and GPU, but optimizing for neural processing units (NPUs) is still challenging due to the heterogeneous compute units and complicated memory hierarchy.
Optical manipulation of electronic dimensionality in a quantum material
Exotic phenomenon can be achieved in quantum materials by confining electronic states into two dimensions.
Strongly Correlated Electrons Materials Science Superconductivity
Interpretable and Transferable Models to Understand the Impact of Lockdown Measures on Local Air Quality
In this work, we estimate pollution reduction over the lockdown period by using the measurements from ground air pollution monitoring stations, training a long-term prediction model and comparing its predictions to measured values over the lockdown month. We show that our models achieve state-of-the-art performance on the data from air pollution measurement stations in Switzerland and in China: evaluate up to -15. 8% / +34. 4% change in NO2 / PM10 in Zurich; -35. 3 % / -3. 5 % and -42. 4 % / -34. 7 % in NO2 / PM2. 5 in Beijing and Wuhan respectively.
Adaptive Loss-aware Quantization for Multi-bit Networks
We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms.
