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Found 14 papers, 5 papers with code
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks
However, one issue that often arises in MTL is the convergence speed between tasks varies due to differences in task difficulty, so it can be a challenge to simultaneously achieve the best performance on all tasks with a single model checkpoint.
Data Augmentation for Sample Efficient and Robust Document Ranking
We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model.
Earthquake Magnitude and b value prediction model using Extreme Learning Machine
The testing RMSE came out to be around $0. 097$.
Learning Invariant Representations for Equivariant Neural Networks Using Orthogonal Moments
The final classification layer in equivariant neural networks is invariant to different affine geometric transformations such as rotation, reflection and translation, and the scalar value is obtained by either eliminating the spatial dimensions of filter responses using convolution and down-sampling throughout the network or average is taken over the filter responses.
Towards Axiomatic Explanations for Neural Ranking Models
We investigate whether one can explain the behavior of neural ranking models in terms of their congruence with well understood principles of document ranking by using established theories from axiomatic IR.
BERTnesia: Investigating the capture and forgetting of knowledge in BERT
We found that ranking models forget the least and retain more knowledge in their final layer compared to masked language modeling and question-answering.
Dissonance Between Human and Machine Understanding
Are humans consistently better at selecting features that make image recognition more accurate?
Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network
Motivation: RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization.
BERTnesia: Investigating the capture and forgetting of knowledge in BERT
We found that ranking models forget the least and retain more knowledge in their final layer.
Valid Explanations for Learning to Rank Models
In extensive quantitative experiments we show that our approach outperforms other model agnostic explanation approaches across pointwise, pairwise and listwise LTR models in validity while not compromising on completeness.
Toxicity Prediction by Multimodal Deep Learning
Prediction of toxicity levels of chemical compounds is an important issue in Quantitative Structure-Activity Relationship (QSAR) modeling.
A study on the Interpretability of Neural Retrieval Models using DeepSHAP
In image classification, the reference input tends to be a plain black image.
Asynchronous Training of Word Embeddings for Large Text Corpora
In this paper, we propose a scalable approach to train word embeddings by partitioning the input space instead in order to scale to massive text corpora while not sacrificing the performance of the embeddings.
