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Found 9 papers, 4 papers with code
Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?
The rapid advancement of Large Language Models (LLMs) has led to extensive discourse regarding their potential to boost the return of quantitative stock trading strategies.
PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout
Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements, including text, logo, and underlay, which is a key to automatic template-free creative graphic design.
You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms
Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural networks (DNNs).
SymNMF-Net for The Symmetric NMF Problem
Furthermore, we analyze the constraints of the inversion layer to ensure the output stability of the network to a certain extent.
Fast and Differentiable Matrix Inverse and Its Extension to SVD
First of all, to perform matrix inverse, we provide a differentiable yet efficient way, named LD-Minv, which is a learnable deep neural network (DNN) with each layer being an $L$-th order matrix polynomial.
Bringing AI To Edge: From Deep Learning's Perspective
To bridge the gap, a plethora of deep learning techniques and optimization methods are proposed in the past years: light-weight deep learning models, network compression, and efficient neural architecture search.
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph
On the one hand, sparse KGs contain less information, which makes it difficult for the model to choose correct paths.
Maximum-and-Concatenation Networks
While successful in many fields, deep neural networks (DNNs) still suffer from some open problems such as bad local minima and unsatisfactory generalization performance.
Tensor Q-Rank: New Data Dependent Definition of Tensor Rank
Recently, the \textit{Tensor Nuclear Norm~(TNN)} regularization based on t-SVD has been widely used in various low tubal-rank tensor recovery tasks.
