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Found 15 papers, 1 papers with code
Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning
However, its performance is very dependent on the training data and performs poorly on data outside the training set, which leads to poor noise robustness and Versatility of such methods.
MMSR: Symbolic Regression is a Multimodal Task
The SR problem is solved as a pure multimodal problem, and contrastive learning is also introduced in the training process for modal alignment to facilitate later modal feature fusion.
PL-FSCIL: Harnessing the Power of Prompts for Few-Shot Class-Incremental Learning
In this paper, we propose a novel approach called Prompt Learning for FSCIL (PL-FSCIL), which harnesses the power of prompts in conjunction with a pre-trained Vision Transformer (ViT) model to address the challenges of FSCIL effectively.
PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression
Therefore, a greedy pruning algorithm is proposed to prune the network into a subnetwork while ensuring the accuracy of data fitting.
Discovering Mathematical Formulas from Data via GPT-guided Monte Carlo Tree Search
To optimize the trade-off between efficiency and versatility, we introduce SR-GPT, a novel algorithm for symbolic regression that integrates Monte Carlo Tree Search (MCTS) with a Generative Pre-Trained Transformer (GPT).
A Novel Paradigm for Neural Computation: X-Net with Learnable Neurons and Adaptable Structure
1, The type of activation function is single and relatively fixed, which leads to poor "unit representation ability" of the network, and it is often used to solve simple problems with very complex networks; 2, the network structure is not adaptive, it is easy to cause network structure redundant or insufficient.
MetaSymNet: A Dynamic Symbolic Regression Network Capable of Evolving into Arbitrary Formulations
To address these issues, we propose MetaSymNet, a novel neural network that dynamically adjusts its structure in real-time, allowing for both expansion and contraction.
A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data
Instead of searching for expressions within a large search space, we explore DySymNet with various structures and optimize them to identify expressions that better-fitting the data.
A Survey on Few-Shot Class-Incremental Learning
Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective.
Learning Continuous Face Representation with Explicit Functions
First, we propose an explicit model (EmFace) for human face representation in the form of a finite sum of mathematical terms, where each term is an analytic function element.
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection
In recent studies, Lots of work has been done to solve time series anomaly detection by applying Variational Auto-Encoders (VAEs).
Visual-speech Synthesis of Exaggerated Corrective Feedback
To provide more discriminative feedback for the second language (L2) learners to better identify their mispronunciation, we propose a method for exaggerated visual-speech feedback in computer-assisted pronunciation training (CAPT).
GmFace: A Mathematical Model for Face Image Representation Using Multi-Gaussian
The GmNet is then designed using Gaussian functions as neurons, with parameters that correspond to each of the parameters of GmFace in order to transform the problem of GmFace parameter solving into a network optimization problem of GmNet.
A Local Descriptor with Physiological Characteristic for Finger Vein Recognition
In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i. e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition.
Continuous learning of face attribute synthesis
To overcome the limitations of a single network in new attribute synthesis, a continuous learning method for face attribute synthesis is proposed in this work.
