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CLI tools, utilities, converters, workflow automation, and productivity helpers
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Showing 17,233–17,256 of 49,285 skills
- Molecular Classification Using Hyperdimensional Graph Classification**arXiv ID:** 2403.12307 **Authors:** Pere Verges, Igor Nunes, Mike Heddes, Tony Givargis, Alexandru Nicolau **Published:** 2024-03-18T23:16:17Z **Abstract:** Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. Graphs serve as a widely embraced method for conveying information, and their utilization in learning has gained significant attention. This is notable in the field of chemoinformatics, where learning from graph representations plays a...Votes: 0GitHub stars: 3
- Mode Regularized Generative Adversarial Networks**arXiv ID:** 1612.02136 **Authors:** Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, Wenjie Li **Published:** 2016-12-07T07:45:38Z **Abstract:** Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make train...Votes: 0GitHub stars: 3
- Mitigation Of Gender Bias In Automatic Facial Nonverbal Behaviors Generation**arXiv ID:** 2410.07274 **Authors:** Alice Delbosc, Magalie Ochs, Nicolas Sabouret, Brian Ravenet, Stephane Ayache **Published:** 2024-10-09T06:41:24Z **Abstract:** Research on non-verbal behavior generation for social interactive agents focuses mainly on the believability and synchronization of non-verbal cues with speech. However, existing models, predominantly based on deep learning architectures, often perpetuate biases inherent in the training data. This raises ethical concerns, dependi...Votes: 0GitHub stars: 3
- Metalearning With Hebbian Fast Weights**arXiv ID:** 1807.05076 **Authors:** Tsendsuren Munkhdalai, Adam Trischler **Published:** 2018-07-12T14:40:06Z **Abstract:** We unify recent neural approaches to one-shot learning with older ideas of associative memory in a model for metalearning. Our model learns jointly to represent data and to bind class labels to representations in a single shot. It builds representations via slow weights, learned across tasks through SGD, while fast weights constructed by a Hebbian learning rule impleme...Votes: 0GitHub stars: 3
- Linear Constraints Learning For Spiking Neurons**arXiv ID:** 2103.12564 **Authors:** Huy Le Nguyen, Dominique Chu **Published:** 2021-03-10T13:54:05Z **Abstract:** We introduce a new supervised learning algorithm based to train spiking neural networks for classification. The algorithm overcomes a limitation of existing multi-spike learning methods: it solves the problem of interference between interacting output spikes during a learning trial. This problem of learning interference causes learning performance in existing approaches to decr...Votes: 0GitHub stars: 3
- Layerwise Geodistributed Computing Between Cloud And Iot**arXiv ID:** 2201.07215 **Authors:** Satoshi Kamo, Yiqiang Sheng **Published:** 2022-01-14T23:41:04Z **Abstract:** In this paper, we propose a novel architecture for a deep learning system, named k-degree layer-wise network, to realize efficient geo-distributed computing between Cloud and Internet of Things (IoT). The geo-distributed computing extends Cloud to the geographical verge of the network in the neighbor of IoT. The basic ideas of the proposal include a k-degree constraint and a lay...Votes: 0GitHub stars: 3
- Feedback Favors The Generalization Of Neural Odes**arXiv ID:** 2410.10253 **Authors:** Jindou Jia, Zihan Yang, Meng Wang, Kexin Guo, Jianfei Yang, Xiang Yu, Lei Guo **Published:** 2024-10-14T08:09:45Z **Abstract:** The well-known generalization problem hinders the application of artificial neural networks in continuous-time prediction tasks with varying latent dynamics. In sharp contrast, biological systems can neatly adapt to evolving environments benefiting from real-time feedback mechanisms. Inspired by the feedback philosophy, we presen...Votes: 0GitHub stars: 3
- Evolution Meets Diffusion Efficient Neural Architecture Generation**arXiv ID:** 2504.17827 **Authors:** Bingye Zhou, Caiyang Yu, Chenwei Tang **Published:** 2025-04-24T03:09:04Z **Abstract:** Neural Architecture Search (NAS) has gained widespread attention for its transformative potential in deep learning model design. However, the vast and complex search space of NAS leads to significant computational and time costs. Neural Architecture Generation (NAG) addresses this by reframing NAS as a generation problem, enabling the precise generation of optimal arch...Votes: 0GitHub stars: 3
- Erfact And Pserf Nonmonotonic Smooth Trainable Activation Functions**arXiv ID:** 2109.04386 **Authors:** Koushik Biswas, Sandeep Kumar, Shilpak Banerjee, Ashish Kumar Pandey **Published:** 2021-09-09T16:17:38Z **Abstract:** An activation function is a crucial component of a neural network that introduces non-linearity in the network. The state-of-the-art performance of a neural network depends also on the perfect choice of an activation function. We propose two novel non-monotonic smooth trainable activation functions, called ErfAct and Pserf. Experiments su...Votes: 0GitHub stars: 3
- Eegsn Towards Efficient Lowlatency Decoding Of Eeg With Graph Spiking Neural Networks**arXiv ID:** 2304.07655 **Authors:** Xi Chen, Siwei Mai, Konstantinos Michmizos **Published:** 2023-04-15T23:30:17Z **Abstract:** A vast majority of spiking neural networks (SNNs) are trained based on inductive biases that are not necessarily a good fit for several critical tasks that require low-latency and power efficiency. Inferring brain behavior based on the associated electroenchephalography (EEG) signals is an example of how networks training and inference efficiency can be heavily im...Votes: 0GitHub stars: 3
- Arxiv 2609 10525v1 Characterizing Language Generation In The Limit Fi**arXiv ID:** 2609.10525v1 **Authors:** Xiaoyu Li, Andi Han, Jiaojiao Jiang, Junbin Gao **URL:** http://arxiv.org/abs/2609.10525v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 10350v1 Cyber Financial Contagion Modeling The Propagation**arXiv ID:** 2609.10350v1 **Authors:** Alex Leytes **URL:** http://arxiv.org/abs/2609.10350v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 10296v1 The Semantic Bottleneck Leveraging Semantic Repres**arXiv ID:** 2609.10296v1 **Authors:** Gilad D. Landau, Dulhan Jayalath, Oiwi Parker Jones **URL:** http://arxiv.org/abs/2609.10296v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 10210v1 Through The Looking Glass Directly Reading And Wri**arXiv ID:** 2609.10210v1 **Authors:** Mark Oskin **URL:** http://arxiv.org/abs/2609.10210v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09856v1 With A Thermomix You Lose The Ability To Cook A Ki**arXiv ID:** 2609.09856v1 **Authors:** Nikol Rummel, Valentina Nachtigall, Ernesto Panadero **URL:** http://arxiv.org/abs/2609.09856v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09848v1 Subgroup Membership Inference Audits Of Differenti**arXiv ID:** 2609.09848v1 **Authors:** Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar, Siew Kei Lam, Anil Anthony Bharath **URL:** http://arxiv.org/abs/2609.09848v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09768v1 Fine Tuning A Kv Cache Concatenation Aware Model O**arXiv ID:** 2609.09768v1 **Authors:** Fumihiko Tachibana, Daisuke Miyashita, Jun Deguchi **URL:** http://arxiv.org/abs/2609.09768v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09656v1 Why Learning Rediscovers The Closed Form Diagonal**arXiv ID:** 2609.09656v1 **Authors:** Jeahn Han, Pyojin Kim **URL:** http://arxiv.org/abs/2609.09656v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09589v1 A Function Space Approach To The Statistical Mecha**arXiv ID:** 2609.09589v1 **Authors:** Yizhou Zhang, Weichen Wu, Lun Du, Zhengjie Miao **URL:** http://arxiv.org/abs/2609.09589v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09094v1 The Surprising Effectiveness Of Approximate Value**arXiv ID:** 2609.09094v1 **Authors:** Raphael Boige, Amine Boumaza, Bruno Scherrer **URL:** http://arxiv.org/abs/2609.09094v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09048v1 The Audit Decides The Verdict Instrument Effects R**arXiv ID:** 2609.09048v1 **Authors:** Siddharth Vohra, Manikandan Ravikiran **URL:** http://arxiv.org/abs/2609.09048v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09011v1 Closed Form Of The Local Galactic Potential And St**arXiv ID:** 2609.09011v1 **Authors:** Indranil Das, Adam Kamoski, Dora Demiri, Brianna Isola, Hanieh Karimi, Dmitrii S. Zagorulia **URL:** http://arxiv.org/abs/2609.09011v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 08832v1 Closing The Consistency Gap Self Evolving Agents T**arXiv ID:** 2609.08832v1 **Authors:** Evelyn Duesterwald, Benjamin Elder, Lilian Ngweta, Shashanka Ubaru, Malgorzata Zimon **URL:** http://arxiv.org/abs/2609.08832v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 08772v1 It S All In The Way You Say It The Role Of Informa**arXiv ID:** 2609.08772v1 **Authors:** Andrea Apicella, Pasquale Arpaia, Matteo Orefice, Andrea Pollastro, Roberto Prevete **URL:** http://arxiv.org/abs/2609.08772v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3