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Found 12 papers, 4 papers with code
Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching
In this paper, we present a novel deep learning framework that derives discriminative local descriptors for 3D surface shapes.
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
If the function is incompletely-specified, the implementation has to be true only on the care set.
NeRV: Neural Representations for Videos
In contrast, with NeRV, we can use any neural network compression method as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches (H. 264, HEVC \etc).
Ranked #6 on
Video Reconstruction
on UVG
Neural Space-filling Curves
We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images.
Deep learning forward and reverse primer design to detect SARS-CoV-2 emerging variants
Hence, in this paper, we develop a semi-automated method to design both forward and reverse primer sets to detect SARS-CoV-2 variants.
NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling
This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video.
Towards Scalable Neural Representation for Diverse Videos
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e. g., NeRV, E-NeRV).
Chop & Learn: Recognizing and Generating Object-State Compositions
Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions.
PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning
However, two roadblocks arise for training a DRL policy that outputs paths: (1) The action space for potential paths often involves higher dimensions comparing to low-level commands, which increases the difficulties of training; (2) It takes multiple time steps to track a path instead of a single time step, which requires the path to predicate the interactions of the robot w. r. t.
Multimodality-guided Image Style Transfer using Cross-modal GAN Inversion
Compared to IIST, such approaches provide more flexibility with text-specified styles, which are useful in scenarios where the style is hard to define with reference images.
Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs
In this study, we introduce a pluggable CTG framework for Large Language Models (LLMs) named Dynamic Attribute Graphs-based controlled text generation (DATG).
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint
Solving image inverse problems (e. g., super-resolution and inpainting) requires generating a high fidelity image that matches the given input (the low-resolution image or the masked image).
