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CLI tools, utilities, converters, workflow automation, and productivity helpers
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- Arxiv 2006 04156 Analogy As Nonparametric Bayesian Inference Over RAnalogy as Nonparametric Bayesian Inference over Relational Systems (arXiv: 2006.04156)Votes: 0GitHub stars: 3
- Arxiv 2002 04806 The Unreasonable Effectiveness Of Deep Learning InThe Unreasonable Effectiveness of Deep Learning in Artificial Intelligence (arXiv: 2002.04806)Votes: 0GitHub stars: 3
- Arxiv 1911 08584 Neocortical Plasticity An Unsupervised Cake But NoNeocortical plasticity: an unsupervised cake but no free lunch (arXiv: 1911.08584)Votes: 0GitHub stars: 3
- Arxiv 1807 00053 Task Driven Convolutional Recurrent Models Of TheTask-Driven Convolutional Recurrent Models of the Visual System (arXiv: 1807.00053)Votes: 0GitHub stars: 3
- Arxiv 1407 3501 Robots That Can Adapt Like AnimalsRobots that can adapt like animals (arXiv: 1407.3501)Votes: 0GitHub stars: 3
- Arxiv 1308 2350 Learning Features And Their Transformations By SpaLearning Features and their Transformations by Spatial and Temporal Spherical Clustering (arXiv: 1308.2350)Votes: 0GitHub stars: 3
- Approximate Answering Of Graph Queries**arXiv ID:** 2308.06585 **Authors:** Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan, Max Berrendorf, Daniel Daza, Mikhail Galkin, Pasquale Minervini, Mathias Niepert, Hongyu Ren **Published:** 2023-08-12T14:47:21Z **Abstract:** Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and evolves, making existing facts deprecated or introducing new ones. However, we w...Votes: 0GitHub stars: 3
- Application Of Unsupervised Artificial Neural Network Ann Selforganizing Map Som In Identifying Main Car Sales Factors**arXiv ID:** 2408.05110 **Authors:** Mazyar Taghavi **Published:** 2024-07-29T14:24:16Z **Abstract:** Factors which attract customers and persuade them to buy new car are various regarding different consumer tastes. There are some methods to extract pattern form mass data. In this case we firstly asked passenger car marketing experts to rank more important factors which affect customer decision making behavior using fuzzy Delphi technique, then we provided a sample set from questionnaires an...Votes: 0GitHub stars: 3
- An Online Supervised Learning Algorithm Based On Triple Spikes For Spiking Neural Networks**arXiv ID:** 1901.01549 **Authors:** Guojun Chen, Xianghong Lin, Guoen Wang **Published:** 2019-01-06T15:11:10Z **Abstract:** Using precise times of every spike, spiking supervised learning has more effects on complex spatial-temporal pattern than supervised learning only through neuronal firing rates. The purpose of spiking supervised learning after spatial-temporal encoding is to emit desired spike trains with precise times. Existing algorithms of spiking supervised learning have excellent...Votes: 0GitHub stars: 3
- Adnev A Scalable Multilevel Neuroevolution Framework For Multivariate Anomaly Detection**arXiv ID:** 2305.16497 **Authors:** Marcin Pietron, Dominik Zurek, Kamil Faber, Roberto Corizzo **Published:** 2023-05-25T21:52:38Z **Abstract:** Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time consuming process. Neuroevolution could be an effective and efficient solution ...Votes: 0GitHub stars: 3
- Accelerating Training Speed Of Tiny Recursive Models With Curriculum Guided Adaptive Recursion**arXiv ID:** 2511.08653 **Authors:** Kaleem Ullah Qasim, Jiashu Zhang **Published:** 2025-11-11T08:17:23Z **Abstract:** Background: Recursive reasoning models achieve strong performance through iterative refinement, allowing small networks to match large language models. However, training is computationally expensive, often requiring 36 GPU-hours for Sudoku extreme. Existing models use fixed recursion depth and uniform supervision weighting, leading to inefficient training. Objectives: We pr...Votes: 0GitHub stars: 3
- A Temporal Anomaly Detection System For Vehicles Utilizing Functional Working Groups And Sensor Channels**arXiv ID:** 2209.06828 **Authors:** Subash Neupane, Ivan A. Fernandez, Wilson Patterson, Sudip Mittal, Shahram Rahimi **Published:** 2022-09-14T14:33:07Z **Abstract:** A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs). Examples of these FWGs include the engine system, transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels that gauge vehicular...Votes: 0GitHub stars: 3
- A Scalable Test Problem Generator For Sequential Transfer Optimization**arXiv ID:** 2304.08503 **Authors:** Xiaoming Xue, Cuie Yang, Liang Feng, Kai Zhang, Linqi Song, Kay Chen Tan **Published:** 2023-04-17T06:48:07Z **Abstract:** Sequential transfer optimization (STO), which aims to improve the optimization performance on a task of interest by exploiting the knowledge captured from several previously-solved optimization tasks stored in a database, has been gaining increasing research attention over the years. However, despite the remarkable advances in algorit...Votes: 0GitHub stars: 3
- A Perspective On Objects And Systematic Generalization In Modelbased Rl**arXiv ID:** 1906.01035 **Authors:** Sjoerd van Steenkiste, Klaus Greff, Jürgen Schmidhuber **Published:** 2019-06-03T19:29:12Z **Abstract:** In order to meet the diverse challenges in solving many real-world problems, an intelligent agent has to be able to dynamically construct a model of its environment. Objects facilitate the modular reuse of prior knowledge and the combinatorial construction of such models. In this work, we argue that dynamically bound features (objects) do not simply em...Votes: 0GitHub stars: 3
- A Novel Progressive Learning Technique For Multiclass Classification**arXiv ID:** 1609.00085 **Authors:** Rajasekar Venkatesan, Meng Joo Er **Published:** 2016-09-01T01:50:18Z **Abstract:** In this paper, a progressive learning technique for multi-class classification is proposed. This newly developed learning technique is independent of the number of class constraints and it can learn new classes while still retaining the knowledge of previous classes. Whenever a new class (non-native to the knowledge learnt thus far) is encountered, the neural network struc...Votes: 0GitHub stars: 3
- A Novel Neural Networkbased Federated Learning System For Imbalanced And Noniid Data**arXiv ID:** 2311.10025 **Authors:** Mahfuzur Rahman Chowdhury, Muhammad Ibrahim **Published:** 2023-11-16T17:14:07Z **Abstract:** With the growth of machine learning techniques, privacy of data of users has become a major concern. Most of the machine learning algorithms rely heavily on large amount of data which may be collected from various sources. Collecting these data yet maintaining privacy policies has become one of the most challenging tasks for the researchers. To combat this issue,...Votes: 0GitHub stars: 3
- A Multiagent Framework For The Asynchronous And Collaborative Extension Of Multitask Ml Systems**arXiv ID:** 2209.14745 **Authors:** Andrea Gesmundo **Published:** 2022-09-29T13:02:58Z **Abstract:** The traditional ML development methodology does not enable a large number of contributors, each with distinct objectives, to work collectively on the creation and extension of a shared intelligent system. Enabling such a collaborative methodology can accelerate the rate of innovation, increase ML technologies accessibility and enable the emergence of novel capabilities. We believe that this...Votes: 0GitHub stars: 3
- A Library Of Mirrors Deep Neural Nets In Low Dimensions Are Convex Lasso Models With Reflection Features**arXiv ID:** 2403.01046 **Authors:** Emi Zeger, Yifei Wang, Aaron Mishkin, Tolga Ergen, Emmanuel Candès, Mert Pilanci **Published:** 2024-03-02T00:33:45Z **Abstract:** We prove that training neural networks on 1-D data is equivalent to solving convex Lasso problems with discrete, explicitly defined dictionary matrices. We consider neural networks with piecewise linear activations and depths ranging from 2 to an arbitrary but finite number of layers. We first show that two-layer networks with...Votes: 0GitHub stars: 3
- A Generative Model For Molecule Generation Based On Chemical Reaction Trees**arXiv ID:** 2106.03394 **Authors:** Dai Hai Nguyen, Koji Tsuda **Published:** 2021-06-07T07:47:18Z **Abstract:** Deep generative models have been shown powerful in generating novel molecules with desired chemical properties via their representations such as strings, trees or graphs. However, these models are limited in recommending synthetic routes for the generated molecules in practice. We propose a generative model to generate molecules via multi-step chemical reaction trees. Specificall...Votes: 0GitHub stars: 3
- Value Iteration Networks**arXiv ID:** 1602.02867 **Authors:** Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel **Published:** 2016-02-09T05:44:36Z **Abstract:** We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the v...Votes: 0GitHub stars: 3
- Understanding Textual Emotion Through Emoji Prediction**arXiv ID:** 2508.10222 **Authors:** Ethan Gordon, Nishank Kuppa, Rigved Tummala, Sriram Anasuri **Published:** 2025-08-13T22:17:00Z **Abstract:** This project explores emoji prediction from short text sequences using four deep learning architectures: a feed-forward network, CNN, transformer, and BERT. Using the TweetEval dataset, we address class imbalance through focal loss and regularization techniques. Results show BERT achieves the highest overall performance due to its pre-training adv...Votes: 0GitHub stars: 3
- Trl Fine TuningFine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.Votes: 0GitHub stars: 3
- Stochastic Neural Networks For Hierarchical Reinforcement Learning**arXiv ID:** 1704.03012 **Authors:** Carlos Florensa, Yan Duan, Pieter Abbeel **Published:** 2017-04-10T18:41:28Z **Abstract:** Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in d...Votes: 0GitHub stars: 3
- Snuffy Efficient Whole Slide Image Classifier**arXiv ID:** 2408.08258 **Authors:** Hossein Jafarinia, Alireza Alipanah, Danial Hamdi, Saeed Razavi, Nahal Mirzaie, Mohammad Hossein Rohban **Published:** 2024-08-15T16:59:15Z **Abstract:** Whole Slide Image (WSI) classification with multiple instance learning (MIL) in digital pathology faces significant computational challenges. Current methods mostly rely on extensive self-supervised learning (SSL) for satisfactory performance, requiring long training periods and considerable computationa...Votes: 0GitHub stars: 3