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Found 29 papers, 12 papers with code
Reliability quality measures for recommender systems
Both quality measures are based on the hypothesis that the more suitable a reliability measure is, the better accuracy results it will provide when applied.
CF4J: Collaborative Filtering for Java
Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem.
Fluid Simulation on Neural Flow Maps
We introduce Neural Flow Maps, a novel simulation method bridging the emerging paradigm of implicit neural representations with fluid simulation based on the theory of flow maps, to achieve state-of-the-art simulation of inviscid fluid phenomena.
Inferring Hybrid Neural Fluid Fields from Videos
Specifically, to deal with visual ambiguities of fluid velocity, we introduce a set of physics-based losses that enforce inferring a physically plausible velocity field, which is divergence-free and drives the transport of density.
Skywork: A More Open Bilingual Foundation Model
In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3. 2 trillion tokens drawn from both English and Chinese texts.
SkyMath: Technical Report
Large language models (LLMs) have shown great potential to solve varieties of natural language processing (NLP) tasks, including mathematical reasoning.
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz
We compare our proposed approach with baseline methods: Monte-Carlo dropout and deep ensembles, and further analysis included MRI denoising and Computed Tomography (CT) sparse-to-full view reconstruction using UNET architectures.
Learning Neural Duplex Radiance Fields for Real-Time View Synthesis
Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual quality.
Neural Partial Differential Equations with Functional Convolution
We present a lightweighted neural PDE representation to discover the hidden structure and predict the solution of different nonlinear PDEs.
FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation
We identify several challenges for fluid manipulation learning by evaluating a set of reinforcement learning and trajectory optimization methods on our platform.
Learning Vortex Dynamics for Fluid Inference and Prediction
We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video.
EASpace: Enhanced Action Space for Policy Transfer
In this paper, a novel algorithm named EASpace (Enhanced Action Space) is proposed, which formulates macro actions in an alternative form to accelerate the learning process using multiple available sub-optimal expert policies.
FSID: Fully Synthetic Image Denoising via Procedural Scene Generation
To address this, we have developed a procedural synthetic data generation pipeline and dataset tailored to low-level vision tasks.
Symplectically Integrated Symbolic Regression of Hamiltonian Dynamical Systems
Here we present Symplectically Integrated Symbolic Regression (SISR), a novel technique for learning physical governing equations from data.
Learning Spatio-Temporal Downsampling for Effective Video Upscaling
Consequently, the inverse task of upscaling a low-resolution, low frame-rate video in space and time becomes a challenging ill-posed problem due to information loss and aliasing artifacts.
On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapy
The gold-standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real-time adaptation.
IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation
IFlyEA provides multi-level and multi-dimension analytical modules for essay assessment.
Tree frog-inspired nanopillar arrays for enhancement of adhesion and friction
Bioinspired structure adhesives have received increasing interest for many applications, such as climbing robots and medical devices.
Soft Condensed Matter
VortexNet: Learning Complex Dynamic Systems with Physics-Embedded Networks
Since the number of such vortices are much smaller than that of the Eulerian, grid discretization, this Lagrangian discretization in essence encodes the system dynamics on a compact physics-based latent space.
Nonseparable Symplectic Neural Networks
The enabling mechanics of our approach is an augmented symplectic time integrator to decouple the position and momentum energy terms and facilitate their evolution.
Learning Physical Constraints with Neural Projections
A neural projection operator lies at the heart of our approach, composed of a lightweight network with an embedded recursive architecture that interactively enforces learned underpinning constraints and predicts the various governed behaviors of different physical systems.
Sparse Symplectically Integrated Neural Networks
We introduce Sparse Symplectically Integrated Neural Networks (SSINNs), a novel model for learning Hamiltonian dynamical systems from data.
RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data
The ability of our model to predict long-term discontinuity from a short window of continuous training data is in general considered impossible using traditional machine learning approaches.
Neural Vortex Method: from Finite Lagrangian Particles to Infinite Dimensional Eulerian Dynamics
To tackle this challenge, we propose a novel learning-based framework, the Neural Vortex Method (NVM), which builds a neural-network description of the Lagrangian vortex structures and their interaction dynamics to reconstruct the high-resolution Eulerian flow field in a physically-precise manner.
Symplectic Neural Networks in Taylor Series Form for Hamiltonian Systems
We propose an effective and lightweight learning algorithm, Symplectic Taylor Neural Networks (Taylor-nets), to conduct continuous, long-term predictions of a complex Hamiltonian dynamic system based on sparse, short-term observations.
Lagrangian-Eulerian Multi-Density Topology Optimization with the Material Point Method
A quadrature-level connectivity graph-based method is adopted to avoid the artificial checkerboard issues commonly existing in multi-resolution topology optimization methods.
Computational Physics Computational Engineering, Finance, and Science Graphics
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud Processing
This paper presents a novel physics-inspired deep learning approach for point cloud processing motivated by the natural flow phenomena in fluid mechanics.
MR fingerprinting Deep RecOnstruction NEtwork (DRONE)
The accuracy of the NN reconstruction of noisy data is compared to conventional MRF template matching as a function of training data size, and quantified in a both simulated numerical brain phantom data and acquired data from the ISMRM/NIST phantom.
Image reconstruction by domain transform manifold learning
Image reconstruction plays a critical role in the implementation of all contemporary imaging modalities across the physical and life sciences including optical, MRI, CT, PET, and radio astronomy.
