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Found 8 papers, 3 papers with code
Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network
The opioid epidemic, referring to the growing hospitalizations and deaths because of overdose of opioid usage and addiction, has become a severe health problem in the United States.
Learning the Market: Sentiment-Based Ensemble Trading Agents
We propose the integration of sentiment analysis and deep-reinforcement learning ensemble algorithms for stock trading, and design a strategy capable of dynamically altering its employed agent given concurrent market sentiment.
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
While the model parameters do contribute to memory usage, the primary memory bottleneck during training arises from storing feature maps, also known as activations, as they are crucial for gradient calculation.
Improving CNN-base Stock Trading By Considering Data Heterogeneity and Burst
In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i. e., constructing automatic and intelligent trading framework based on historical stock price.
Revisit Kernel Pruning with Lottery Regulated Grouped Convolutions
In this paper, we revisit the idea of kernel pruning (to only prune one or several $k \times k$ kernels out of a 3D-filter), a heavily overlooked approach under the context of structured pruning due to it will naturally introduce sparsity to filters within the same convolutional layer—thus, making the remaining network no longer dense.
Grad-CAM guided channel-spatial attention module for fine-grained visual classification
The proposed method outperforms the SOTA attention modules in the FGVC task.
Ranked #21 on
Fine-Grained Image Classification
on FGVC Aircraft
Quantum-Inspired Classical Algorithm for Principal Component Regression
In this work, we developed an algorithm for principal component regression that runs in time polylogarithmic to the number of data points, an exponential speed up over the state-of-the-art algorithm, under the mild assumption that the input is given in some data structure that supports a norm-based sampling procedure.
A modified Physarum-inspired model for the user equilibrium traffic assignment problem
The user equilibrium traffic assignment principle is very important in the traffic assignment problem.
