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Found 7 papers, 2 papers with code
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.
An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions
Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements.
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.
Irrelevant Pixels are Everywhere: Find and Exclude Them for More Efficient Computer Vision
We observe that, given a computer vision task, images often contain pixels that are irrelevant to the task.
Give me a knee radiograph, I will tell you where the knee joint area is: a deep convolutional neural network adventure
Knee pain is undoubtedly the most common musculoskeletal symptom that impairs quality of life, confines mobility and functionality across all ages.
MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications
We explored semantic similarities and patterns of keyphrases in scientific publications using pre-trained word embedding models.
