Notes

Heparin-bonded compared to regular polytetrafluoroethylene arteriovenous grafts: Any Bayesian viewpoint on the randomized managed tryout pertaining to comparison usefulness.
The frontal lobe double dueling deep Q network (FLD3QN), an innovative approach influenced by the Papez circuit and reinforcement learning neuroscience, uses EEG signals from the frontal lobe as prior information. The FLD3QN framework is built in accordance with the brain's emotional mechanism, with the frontal lobe and thalamus serving as its core, and it utilizes the bifrontal lobe residual convolution neural network (BiFRCNN) to simulate the Papez circuit. To limit the agent's errors, a step penalty mechanism is designed. Public EEG emotion dataset DEAP's ablation studies revealed the frontal lobe and Papez circuit's significance in modeling reward learning during emotional perception, leading to substantial improvements in average valence and arousal prediction accuracy, an increase of 2524% and 2331%, respectively.

To translate a math word problem (MWP) into an executable solution, an MWP solver must convert the problem's narrative into equations. This involves understanding not only the problem's real-world context but also the relationships between quantities and variables, which it must map to a logical equation structure. belnacasan inhibitor In spite of the substantial progress made by deep learning models in MWPs, they commonly disregard the grounding equation logic embedded within the problem statement. Also, pretrained language models (PLMs), as widely recognized, are endowed with a large trove of knowledge and accurate semantic representations, potentially applicable to Multi-Word Problem (MWP) resolution, but their exploration in this specific task has not yet begun. We introduce a template-driven contrastive distillation pretraining (TCDP) approach to extract equation logic and practical knowledge. This approach uses a PLM-based encoder to integrate mathematical logic knowledge through multi-view contrastive learning, while preserving substantial real-world knowledge and a strong semantic representation via knowledge distillation. By our approach, we have christened the pretrained PLM-based encoder MathEncoder. First, symbolic solution templates are clustered within Multiple Word Problems (MWPs) to summarize the mathematical logic. Then, the deployed PLM-based encoder is integrated with this logic, represented by the templates, by using supervised contrastive learning. In the interim, the comprehensive knowledge and superior semantic representations are retained by transferring them from a highly-trained pre-trained language model-based teacher encoder to our MathEncoder. We built a new solver, MathSolver, by integrating our pre-trained MathEncoder into the existing MWP solver GTS, thus replacing the GRU-based encoder to assess its effectiveness. On the widely used benchmarks Math23K and CM17K, our method yields experimental results showing a considerable improvement in a solver's grasp of MWPs, surpassing the performance of previously established state-of-the-art methods. You can find the code for this project on GitHub, located at https://github.com/QinJinghui/tcdp.

Recent endeavors in computer vision have yielded promising results using transformers, leveraging self-attention mechanisms to discern relationships between image fragments. Attention is exclusively applied to a single feature layer, disregarding the potential for a richer understanding that arises from considering attentional mechanisms across multiple layers. In order to boost the efficiency of vision transformers (ViT), we present BViT, a novel technique encompassing broad attention across various layers. Broad attention is realized through the use of a broad connection and parameter-free attention mechanism. The interconnected nature of each transformer layer facilitates the transmission and assimilation of information within BViT. Without introducing additional trainable parameters, parameter-free attention simultaneously analyzes the existing attentional information across various layers to extract pertinent data and define their interconnections. Image classification experiments using BViT demonstrate significantly higher top-1 accuracy on ImageNet, achieving 750% to 816% compared to other models, while using only 5 million to 22 million parameters. Furthermore, we apply BViT to subsequent object recognition benchmarks, achieving 989% accuracy on CIFAR10 and 899% accuracy on CIFAR100, surpassing ViT despite employing fewer parameters. Swin Transformer, T2T-ViT, and LVT all saw an improvement in the generalization test, exceeding 1%, owing to the broad attentional mechanisms. To recap, a broad scope of attention displays a promising potential to bolster the performance of models that leverage attention. At https://github.com/DRL/BViT, you will find the code and pre-trained models.

The task of unlearning data observed during machine learning (ML) model training is crucial for enhancing the privacy and security of ML applications. The piece under consideration prompts the question: can we purge a specific class or classes of data from a machine learning model's existing knowledge without examining the full training dataset? For large datasets and diverse deep networks, can we expedite and scale the process of unlearning? We develop a novel machine unlearning framework that leverages error-maximizing noise generation and impair-repair weight manipulation to effectively address the aforementioned questions. Employing the original model, a noise matrix, crafted to maximize errors, is learned for the class in question to be unlearned. The noise matrix acts upon the model's weights, causing the targeted data class to be forgotten. For controlled network weight manipulation, we introduce impair and repair steps. To facilitate a sharp unlearning process in the model, the impairment stage utilizes a noise matrix and a very high learning rate. Afterward, the repair action is undertaken to recapture the total performance. With only a few update iterations, we showcase remarkable unlearning, while the model's overall accuracy remains substantially intact. The process of dismantling multiple learned concepts demands a comparable number of update procedures as that needed to dismantle a single concept, thus confirming the scalability of our solution for complex scenarios. Our technique is remarkably efficient when compared to existing methods, successfully supporting multiclass unlearning while maintaining flexibility concerning the original optimization scheme and network architecture. Its applicability spans both small and large-scale vision problems with success. This work facilitates a streamlined and expedited method for incorporating unlearning into deep networks. The source code for 'Fast Machine Unlearning' is readily available via the GitHub link: https://github.com/vikram2000b/Fast-Machine-Unlearning.

Invariant representations, free from human annotation, are now frequently generated using self-supervised learning (SSL). Despite this, the desired invariant representation is accomplished by employing prior online transformation functions on the input dataset. Therefore, each SSL framework is specifically crafted for a particular data type, such as visual data, demanding additional modifications if used with other kinds of datasets. On the contrary, autoencoders (AEs), a broadly applicable and common framework, primarily prioritize dimension reduction and are not tailored to learning invariant representations. To prevent degenerate solutions, this article introduces a generic SSL framework, implemented with a constrained self-labeling assignment process. Instead of the preceding transformation functions, a self-transformation mechanism, arising from an unsupervised adversarial training procedure, is introduced to impose invariant representations. Employing a self-transformation method, duplicate input data can be used to generate sets of enhanced instances, each a pair. Ultimately, a training objective grounded in contrastive learning is formulated, utilizing both the self-labeling assignment and the self-transformation approach. Though the self-transformation process is widely applicable, the presented training approach shows superior results compared to the majority of modern state-of-the-art representation learning approaches based on autoencoder designs. In order to validate our methodology's performance, we carried out experiments using four datasets (visual, audio, text, and mass spectrometry) and benchmarked their performance using four quantitative metrics. Results from our comparative study confirm the suggested method's robust and effective pattern recognition capabilities within the evaluated datasets.

The objective of attribute-based person search is to pinpoint the correct person from a set of images, using a textual query for reference. Surveillance systems frequently rely on auditory information, particularly when visual cues are insufficient, for instance, in pinpointing a suspect based on eyewitness accounts. Progress in recent studies, though substantial, frequently neglects the critical attribute labeling problems inherent in current datasets. Compounding the existing problems, these factors further raise the likelihood of a breakdown in alignment between textual attributes and visual images, consequently widening semantic gaps. Addressing the aforementioned issues, this paper presents Weak Semantic Embeddings (WSEs), which alter the data distribution of original attribute texts for better attribute feature representation. To improve our comprehension, we incorporate feature graphs to learn more collaborative and finely-tuned information. The feature graphs, which model relationships among all semantic embeddings, assist in reducing the semantic gap in text-to-image retrieval. The effectiveness of the proposed WSEs is evident through extensive testing on the demanding benchmarks of PETA, Market-1501 Attribute, and PA100K, which demonstrates that our method surpasses the performance of existing state-of-the-art approaches.

A key function within computer vision, salient object detection (SOD), is designed to pinpoint visually striking segments within images.
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