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Found 20 papers, 7 papers with code
Perception-Oriented Video Frame Interpolation via Asymmetric Blending
In practice, motion estimates often prove to be error-prone, resulting in misaligned features.
Towards a Robust Retrieval-Based Summarization System
SummRAG is an example of our goal of defining structured methods to test the capabilities of an LLM, rather than addressing issues in a one-off fashion.
Data Interpreter: An LLM Agent For Data Science
Large Language Model (LLM)-based agents have demonstrated remarkable effectiveness.
Language Agents as Optimizable Graphs
Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on Large Language Models (LLMs), yielding many disparate code bases.
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries.
AccFlow: Backward Accumulation for Long-Range Optical Flow
To train and evaluate the proposed AccFlow, we have constructed a large-scale high-quality dataset named CVO, which provides ground-truth optical flow labels between adjacent and distant frames.
FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware Lookup Table
Experimental results on benchmark datasets demonstrate that our method achieves the State-Of-The-Art (SOTA) performance in terms of both image quality and inter-frame brightness consistency.
Cheap-fake Detection with LLM using Prompt Engineering
We enhance the baseline algorithm by incorporating a Large Language Model (LLM), GPT3. 5, as a feature extractor.
Mindstorms in Natural Language-Based Societies of Mind
What should be the social structure of an NLSOM?
Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge
Efficient automatic segmentation of multi-level (i. e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications.
Optimizing the optimizer for data driven deep neural networks and physics informed neural networks
We investigate the role of the optimizer in determining the quality of the model fit for neural networks with a small to medium number of parameters.
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series
Literature reviews have shown that the LM can be very powerful and effective on moderate function approximation problems when the number of weights in the network is not more than a couple of hundred.
Lossless Point Cloud Attribute Compression with Normal-based Intra Prediction
In this paper, we propose a normal-based intra prediction scheme, which provides a more efficient lossless attribute compression by introducing the normals of point clouds.
RAI-Net: Range-Adaptive LiDAR Point Cloud Frame Interpolation Network
In this paper, we propose a novel LiDAR point cloud frame interpolation method, which exploits range images (RIs) as an intermediate representation with CNNs to conduct the frame interpolation process.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
A Distribution Similarity Based Regularizer for Learning Bayesian Networks
Probabilistic graphical models compactly represent joint distributions by decomposing them into factors over subsets of random variables.
An AI Based Super Nodes Selection Algorithm in BlockChain Networks
In blockchain systems, especially cryptographic currencies such as Bitcoin, the double-spending and Byzantine-general-like problem are solved by reaching consensus protocols among all nodes.
Cryptography and Security
A Formulation of Recursive Self-Improvement and Its Possible Efficiency
Recursive self-improving (RSI) systems have been dreamed of since the early days of computer science and artificial intelligence.
Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni Syndrome
We employed a family-wise likelihood that facilitates using genetic information inherited through the family pedigree and properly adjusted for the ascertainment bias that was inevitable in studies of rare diseases by using an inverse probability weighting scheme.
Applications
Bayesian Optimization Using Monotonicity Information and Its Application in Machine Learning Hyperparameter
We propose an algorithm for a family of optimization problems where the objective can be decomposed as a sum of functions with monotonicity properties.
