1,720,986 research outputs found
Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning
No full textContrastive learning (or its variants) has recently become a promising direction in the self-supervised learning domain, achieving similar performance as supervised learning with minimum fine-tuning. Despite the labeling efficiency, wide and large networks are required to achieve high accuracy, which incurs a high amount of computation and hinders the pragmatic merit of self-supervised learning. To effectively reduce the computation of insignificant features or channels, recent dynamic pruning algorithms for supervised learning employed auxiliary salience predictors. However, we found that such salience predictors cannot be easily trained when they are naïvely applied to contrastive learning from scratch. To address this issue, we propose contrastive dual gating (CDG), a novel dynamic pruning algorithm that skips the uninformative features during contrastive learning without hurting the trainability of the networks. We demonstrate the superiority of CDG with ResNet models for CIFAR-10, CIFAR-100, and ImageNet-100 datasets. Compared to our implementations of state-of-the-art dynamic pruning algorithms for self-supervised learning, CDG achieves up to 15% accuracy improvement for CIFAR-10 dataset with higher computation reduction
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Efficient, Robust, and Self-Supervised Algorithm-Hardware Co-Design
No full text234 pagesThe development of artificial intelligence has revolutionized human society. Computer vision and natural language processing have demonstrated superior performance across a wide range of tasks. From a performance standpoint, the rise of scaling laws has come at the cost of significantly increased computational demands in both the pretraining and post-training phases. The persistent challenges of computational complexity, memory overhead, and energy consumption call for system-level efficiency through hardware–algorithm co-optimization and reliable verification toolkit. Although compression methods have been widely investigated for different model architectures and tasks, jointly optimizing algorithms and specialized AI hardware shows tremendous promise and urgent necessity for both edge- and cloud-based AI computing. Beyond system-level optimization, improving the generality of and understanding the limitations of under-parameterized lightweight model pretraining offers an additional and compelling perspective for efficient AI models and algorithms. Finally, the visual quality degradation resulting from compression and under-parameterized models makes the generative 3D computer vision tasks even more challenging in comparison to the accuracy-driven tasks, whereas the computing and memory resources on VR/AR devices are limited. Motivated by the aforementioned challenges, this doctoral research focuses on the design of efficient and robust AI algorithms and systems, targeting both customized and commercial AI computing platforms for computer vision and language generation applications. Specifically, this dissertation first presents a series of research works based on customized AI In-Memory-Computing (IMC) platforms with Resistive Random Access Memory (ReRAM); as a starting point, this research presents a structured pruning algorithm followed by the dedicated in-memory-computing deployment scheme. While the efficiency has been improved, the non-ideality of the individual ReRAM device and non-reliable retention characteristics destroy the accuracy of the pre-trained model, which motivates the follow-up investigation on algorithm-aided robust enhancement algorithm. In addition to the RRAM-based AI computing, this dissertation also focused on the prue algorithm-level model pruning and efficiency enhancement with supervised and self-supervised learning. For customized hardware, an FPGA-based accelerator is presented with fully-on-chip sparse computing and zero off-chip memory access. On the algorithm side, this research widely explores both supervised and self-supervised sparse training methods, which 1) enable the real-time dynamic sparse model inference and 2) investigate the bottleneck of self-supervised sparse training with contrastive dynamic pruning. Although the various pruning and compression algorithms are widely explored, the compatibility of commercial edge devices are limited. Therefore, directly improving the performance of lightweight models becomes critical. This dissertation presents two novel algorithms which reveals the bottleneck that causes the poor performance of the lightweight model pretraining, focusing on both contrastive learning and masked autoencoder (MAE) training. Apart from the case-by-case customized hardware and algorithm design, this research presents a model compression algorithm designed of drivable 3D Codec Avatar for actual commercial hardware deployment while eliminating the dynamic rendering noises. For the customized hardware, this research introduces Torch2Chip, a generic and end-to-end toolkit for customized hardware-algorithm co-design
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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