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Found 12 papers, 4 papers with code
Large Language Models for Automated Open-domain Scientific Hypotheses Discovery
Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations.
Weakly supervised learning for pattern classification in serial femtosecond crystallography
Th is heavy dependence on labeled datasets will seriously restrict the application of networks, because it is very costly to annotate a large number of diffraction patterns.
Biological Factor Regulatory Neural Network
BFReg-NN starts from gene expression data and is capable of merging most existing biological knowledge into the model, including the regulatory relations among genes or proteins (e. g., gene regulatory networks (GRN), protein-protein interaction networks (PPI)) and the hierarchical relations among genes, proteins and pathways (e. g., several genes/proteins are contained in a pathway).
Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text
It achieved a precision of 88. 37%, a recall of 85. 14%, and an F1-score of 86. 49% on the LLL dataset.
Constrained Reinforcement Learning for Short Video Recommendation
In this paper, we formulate the problem of short video recommendation as a constrained Markov Decision Process (MDP), where platforms want to optimize the main goal of user watch time in long term, with the constraint of accommodating the auxiliary responses of user interactions such as sharing/downloading videos.
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding
We define a maximum traceable distance metric, through which we learn to what extent the text contrastive learning benefits from the historical information of negative samples.
ESOD:Edge-based Task Scheduling for Object Detection
In detail, we train a DNN model (termed as pre-model) to predict which object detection model to use for the coming task and offloads to which edge servers by physical characteristics of the image task (e. g., brightness, saturation).
PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic
In this setting with multiple and constrained goals, this paper discovers that a probabilistic strategic parameter regime can achieve better value compared to the standard regime of finding a single deterministic parameter.
Learning to Remove: Towards Isotropic Pre-trained BERT Embedding
We train the weights on word similarity tasks and show that processed embedding is more isotropic.
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference
We experimentally show that how two mainstream compression techniques, data quantization and pruning, perform on these network architectures and the implications of compression techniques to the model storage size, inference time, energy consumption and performance metrics.
SL$^2$MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization
Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO).
Convolutional neural network based automatic plaque characterization from intracoronary optical coherence tomography images
Traditional machine learning based methods, such as the least squares support vector machine and random forest methods, have been recently employed to automatically characterize plaque regions in OCT images.
