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Found 19 papers, 11 papers with code
Pattern-wise Transparent Sequential Recommendation
However, achieving both model transparency and recommendation performance simultaneously is challenging, especially for models that take the entire sequence of items as input without screening.
Understanding the Role of Cross-Entropy Loss in Fairly Evaluating Large Language Model-based Recommendation
Large language models (LLMs) have gained much attention in the recommendation community; some studies have observed that LLMs, fine-tuned by the cross-entropy loss with a full softmax, could achieve state-of-the-art performance already.
Inner-IoU: More Effective Intersection over Union Loss with Auxiliary Bounding Box
Based on the above, we first analyzed the BBR model and concluded that distinguishing different regression samples and using different scales of auxiliary bounding boxes to calculate losses can effectively accelerate the bounding box regression process.
Ranked #1 on
Object Detection
on AI-TOD
(mAP50 metric)
Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
Embedding plays a critical role in modern recommender systems because they are virtual representations of real-world entities and the foundation for subsequent decision models.
X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs
In this work, we focus on an overall analog-digital architecture implementing a novel increased precision analog CAM and a programmable network on chip allowing the inference of state-of-the-art tree-based ML models, such as XGBoost and CatBoost.
Less Emphasis on Difficult Layer Regions: Curriculum Learning for Singularly Perturbed Convection-Diffusion-Reaction Problems
Although Physics-Informed Neural Networks (PINNs) have been successfully applied in a wide variety of science and engineering fields, they can fail to accurately predict the underlying solution in slightly challenging convection-diffusion-reaction problems.
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction
To be more specific, we first perform a simple degradation of the 3x3 mm2/high-resolution (HR) image to obtain the synthetic LR image.
Understanding Adversarial Robustness from Feature Maps of Convolutional Layers
Our theoretical analysis discovers that larger convolutional feature maps before average pooling can contribute to better resistance to perturbations, but the conclusion is not true for max pooling.
Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions
Deep image inpainting research mainly focuses on constructing various neural network architectures or imposing novel optimization objectives.
Graph-Based Stock Recommendation by Time-Aware Relational Attention Network
For a given group of stocks, the proposed TRAN model can output the ranking results of stocks according to their return ratios.
Missingness Augmentation: A General Approach for Improving Generative Imputation Models
However, data augmentation, as a simple yet effective method, has not received enough attention in this area.
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks
Neural networks are susceptible to artificially designed adversarial perturbations.
Ranked #1 on
Adversarial Attack
on CIFAR-10
Adversarial Momentum-Contrastive Pre-Training
Recently proposed adversarial self-supervised learning methods usually require big batches and long training epochs to extract robust features, which will bring heavy computational overhead on platforms with limited resources.
Revisiting Representation Degeneration Problem in Language Modeling
They call it the representation degeneration problem and propose a cosine regularization to solve it.
Improve Adversarial Robustness via Weight Penalization on Classification Layer
It is well-known that deep neural networks are vulnerable to adversarial attacks.
A Fast deflation Method for Sparse Principal Component Analysis via Subspace Projections
The implementation of conventional sparse principal component analysis (SPCA) on high-dimensional data sets has become a time consuming work.
SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning
It becomes an open question whether escaping sharp minima can improve the generalization.
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
We mathematically prove the convergence of TernGrad under the assumption of a bound on gradients.
Coordinating Filters for Faster Deep Neural Networks
Moreover, Force Regularization better initializes the low-rank DNNs such that the fine-tuning can converge faster toward higher accuracy.
