67 skills found · Page 3 of 3
abhranilchandra / Transfer Learning In RLTransfer Learning in RL to improve sample efficiency
masonjung / Inference Scaling MooSimulationfor optimizing AI inference efficiency using multi-objective optimization (MOO) across cost, latency, and accuracy dimensions, featuring Monte Carlo sampling and constraint-aware feasible region analysis.
chaklam-silpasuwanchai / Organized MLThe repository aims to teaching my students how to best organize your ML code for efficiency and to facilitate experiments. It contains backbone folders and some sample files for illustration.
theovincent / IS DQN🪶iterated Shared Deep Q-Network [ICLR 26], a new algorithm improving the sample-efficiency of target-free algorithms (e.g. DQN IMPALA) to bridge the gap with target-based algorithms🪶
SELGroup / SIDMWe propose a new, unsupervised, and adaptive Decision-Making framework called SIDM for Reinforcement Learning. This approach handles high complexity environments without manual intervention, and increases sample efficiency and policy effectiveness. site at https://selgroup.github.io/SIDM/
beinghorizontal / LSTM CategoricalA sample time series to find LSTM efficiency
fhh2626 / Tutorial Advanced WTM EABFNecessary files for the tutorial "Overcoming Sampling Issues and Improving the Computational Efficiency in Collective-Variable-Based Enhanced Sampling Simulations "
zeph7 / News AppA comprehensive android sample app designed to showcase the best practices in modern android development for scalability, maintainability and efficiency.
BritishGeologicalSurvey / TactoolTACtool is a desktop GUI tool for targeting and co-ordinating sampling points for microanalysis, using spatially resolved imagery, enabling their import directly into sampling systems such as a laser ablation system. The use of this tool dramatically improves analytical traceability and workflow efficiency in the lab.
Novartis / TinydenseRtinydenseR is a landmark-based platform for single cell data analysis that identifies differentially abundant cell types and differentially expressed features, including subtle within-cluster state changes. Modeling samples as replicates, tinydenseR enhances analytic efficiency and reproducibility while preserving the richness of single cell data
nishantb15 / Ray Tracer Part 2 BVH And MeshThis ray tracer builds from my previous ray tracer (https://github.com/nishantb15/Ray-Tracer---Part-1) which implemented simple shapes, hard shadows, Phong Reflection from a point light source, Multi-Jittered Sampling using n-rooks strategy (Anti-aliasing) and Orthographic and Perspective projection with a moveable camera. This ray tracer adds a Bounding Volume Hierarchy (BVH) which is an accelerated structure, in this case, used to generate an image containing 100,000 spheres which would take days to render without a BVH. More information about the BVH is contained in the BVHTable.pdf file. Support is also included to read a .obj file and import a triangular mesh into the scene. The mesh can then be shaded using per-vertex normals as opposed to per-face (1 normal per face). The pictures generated using BVH are generated using 1 for the number of samples in multi-jittered sampling (essentially no anti-aliasing) and no shadows to save cost. The purpose of this project was to test the efficiency of the BVH in generating an enormous amount of spheres and triangles. Part 3 Can be found at: https://github.com/nishantb15/Ray-Tracer-Part-3-Reflection-Refraction-And-Area-Lights
Sun-WeiZhen / Robot Ultrasonic System Based On Deep Reinforcement LearningTraining robot ultrasonic system using deep reinforcement learning.Because ultrasound doctors mainly use discrete motion to control ultrasound probes, most current work is based on DQN. The essential learners we use are also DQN series algorithms, but we have improved the algorithms. Considering the individual differences of patients and the safety of patients, we completed the experiment in the simulation environment. In addition, we also set up the molding reward function and other methods to improve the sample efficiency.
jasonclark / Youtube Digital LibraryMSU Library has created a digital video library using the Youtube API to power our local library channel. It is a complete search and browse app with item level views, microdata, a caching and optimization routine, and a file backup routine. The article will discuss applying the YouTube API as a database application layer, workflow efficiencies gained, metadata procedures as well as local backup and optimization procedures. Code samples in PHP, .htaccess examples, and shell commands used in developing the app and routines will be explained at length. And finally, a complete prototype app will be released on github for other libraries to get started using the lessons learned. A live version of the app is here: http://www.lib.montana.edu/channel/. The real benefit of this method is the low overhead for smaller shops and the ability to scale production and distribution of digital video.
houstoncuj / Educating For The Large Shop To Make Custom Name PatchesIn a needlework shop made for quantity result, an established curriculum should comply with certain principles and a timetable. The larger your embroidery procedure, the much more you need a defined training program. https://houstonembroideryservice.com/custom-patches/ Having your new-hires discover by "on-the-job osmosis" generally leads to irregular task abilities, an unforeseeable timespan to establish trainees and no chance to determine development and also retention. Extra notably, it does not offer your new employees their finest opportunities to stand out. I have handled big, multiple-shift embroidery stores and also found that having a well-known training educational program allowed me to determine where employees needed added direction. A great training program has actually a specified curriculum connected to a timetable. I such as to customize the program to fit my trial-period time frame, which normally is 90 days. At the end of this period, a competent candidate should have successfully finished the program and also have the ability to execute the custom name patches making skills recognized later in this article. EXPERIENCE LEVELS It may be alluring to hire a knowledgeable operator, and also lots of state work commissions currently include a group for embroidery equipment operators. Make sure to completely examine operators that have worked in various other huge shops. Why? Since some huge stores train operators in very details tasks and their general understanding may be limited. For instance, I when hired a seasoned operator from a shop that stitched for Ocean Pacific (OP) Apparel Corp. Nonetheless, when performing sewouts, I learned that she was uninformed that you might move the starting position of the hoop. At her previous shop, jobs were repeated and there was no demand to train particular skills. Still, you can find some excellent skill that might have just recently moved right into your location or a person returning to the workforce. For these reasons, consult your state work compensation. SELECTING A CANDIDATE While many managers look for candidates with sewing experience, remember that industrial stitching equipment drivers are made use of to sitting while working. Embroidery operators need to depend on their feet all the time, proactively moving the workplace. The candidate also must have good eyesight, be able to recognize shade and also be reasonably in shape. I've located a variety of good driver students by seeing their work habits in one more job setup. For instance, when I go to a lunch counter or coffee shop, I notice employees that rush, as well as have knowledge as well as a great perspective. They make fantastic prospects for learning brand-new skills that could result in possibly greater earnings. TRAINING PRINCIPLES When you construct your training program around the complying with ideas, your students will certainly proceed quicker and consistently. 1. The needlework equipment doesn't have a mind of its very own. Makers might occasionally malfunction as a result of an electric or electronic trouble, but such incidents are unusual. When a new trainee states, "I do not recognize why the machine did that," the instructor must respond in a mild way that the device probably did what the trainee advised it to do. This creates responsibility as opposed to advertising the idea that the equipment does strange and also unpredictable points by itself. 2. The needlework machine can harm you. Students, in addition to skilled drivers, need to have a healthy respect for the machine as well as recognize they could be harmed if safety treatments are not complied with. It's an ideal practice to train all drivers to loudly state "Ready" or "Clear" prior to the maker is engaged. This helps guarantee that no fingers are near the needles or in a location where they could be pinched when the pantograph relocations. 3. Mistakes will certainly take place. Stand up to the temptation to jump ahead of your planned training schedule. Doing so can bring about errors-- potentially pricey ones-- and even damage to the tools. When an error does inevitably occur, stay favorable. This is a fine line to stroll due to the fact that you do not want to cultivate the idea that errors are constantly OKAY, however it's also essential to not damage the trainee's morale. Rather, try to make the negative experience a mentor minute. Assist the student comprehend and verbalize what was learned from the experience. 4. Have students say it in their very own words. Lots of people say they comprehend a principle also when they don't. Have the student repeat your instructions for treatments in their very own words. This is a great means to reveal misunderstandings and also miscommunication. Even if you have actually created treatments, allow students to make their very own notes to help them bear in mind the necessary steps to fill a style, designate needles and also other unknown jobs. 5. Most of us do it the same way. Some huge stores have "set-up drivers" and "job operators." In such setups, even more skilled or extra very trained operators set up new tasks, while less-skilled drivers keep the equipment packed as well as threaded. No matter each worker's training, all operators have to comply with the exact same treatments. Even though every person is asked to comply with store standards, no person knows better than drivers where improvements can be made. If a staff member-- also a trainee-- believes a better means exists to do a job, that person ought to feel comfortable sharing it. If it actually is much better, the new approach should come to be basic shop treatment for all workers. APPLICATION It's vital that trainees have the ability to distinguish great as well as inadequate needlework. During the normal course of organization, collect needlework examples that have describes that are off-register, rugged column stitches as well as various other symptoms of inferior needlework. Ask trainees to evaluate these samples to develop their recognition of high-grade stitching. Begin trainees with easy jobs, like altering string for a brand-new task. Next off, progress to mentor tension essentials and also recognizing good needlework from bad embroidery. Make some brief videos of operations in your store and also publish them for either public or private watching on YouTube. This offers a twin function: Trainees will certainly learn from the video clips and also they can show their loved ones concerning their intriguing new task. When creating your training program, accumulate referral material from the Internet, publication short articles or various other relevant resources. Establish treatments for typical tasks and give written standards. ________________________________________. A Minimum Training Plan for Embroidery Machine Operators & Supervisors. Listed here are the minimum elements that must be consisted of in a training program for drivers as well as for managers. Use this list as a guide, and also attach your own timespan as well as sequence that makes good sense for your store. At the end of your trial duration, utilize it as a checklist to evaluate the student's understanding of each element. You'll be pleased with the all-around and also experienced driver you have educated. Digital Embroidery Machine Operators. Student needs to get an explanation for each of the adhering to products and have the ability to carry out after ideal training time. 1) Understanding Placement Standards. a. How to apply your shop's typical embroidery positioning, such as left upper body or complete back. b. Selecting suitable strategies for marking garments when required. 2) Review of Job Details. a. Read orders for efficiency: string shades, design, placement. b. Ask for verification in the case of doubtful punctuation or instructions that don't appear right. 3) Garment Inspection. a. Counting garments. b. Checking for appropriate garments. c. Checking for defects before using embroidery. 4) Hooping. a. Select the smallest hoop that will certainly fit style. b. Exceptions to the guideline, such as maintaining bulky seams out of hoop location. c. Hooping procedures and also preventing damages to material from hooping. d. When to utilize holding fixtures rather than a standard hoop. 5) Matching Stabilizer to Fabrics. a. When to do a test sew-out for an initial post. b. Evaluate for appropriate support. c. Evaluate whether a topping is needed. 6) Assuring Consistent Placement. a. Determine positioning approach strategy for each and every work type. b. How to note garments. 7) Thread Handling. a. Setting up thread for basic work. b. Setting up threads for small quantities or combined color orders. c. Tying of knot to pull through needle for thread transition. d. Tying of knot for thread storage space, when relevant. e. Purpose of each element in the thread path (pre-tensioners, tensioners check springtime). f. How a stitch is created. g. How thread break detector/bobbin sensors work. h. Handling of metallics, polyesters as well as various other specialty strings. 8) Thread Tensions. a. Tension screening procedures (top and bottom). b. Troubleshooting tension problems. c. Adjusting and cleansing of the bobbin instance. d. Adjusting of the upper tensioners. 9) Needles. a. Matching the appropriate needle to items. b. How and when to alter needles. c. Identifying sewing signs and symptoms that are needle-related. 10) Troubleshooting as well as Machine Management. a. When and when not to back up the equipment to repair missing out on string. b. Identifying source of string breaks. c. Lubricating of the maker-- when, where, just how as well as with what. b. Sewing speeds for various tasks and also sew types. 11) Specialty Techniques. a. Producing premium needlework on completed caps. b. Producing appliqué products (if relevant). Needlework Supervisors (Multi-Machine Shops). 1) Pre-Production. a. Scheduling Principles. I. Matching job specifics for reliable consecutive work series. II. Assigning priorities according to assurance date. b. Procedures for purchasing digitized designs. c. Procedures for hosting upcoming orders. 2) Production. a. Sensible, organized job flow through store. b. Monitoring of supplies and also accessories. c. Matching operators to tasks and machines. d. Tracking of production throughout-- preserving a manufacturing log. e. Account daily or weekly losses and expense of nonconformity. 3) Equipment. a. Oversee upkeep. b. Keep a maintenance log for every machine. 4) Training. a. Organize as well as keep recommended reference product for operator students. b. Evaluate students' progression. c. Identify under-skilled drivers and offer aid.
OmSouf / DEA GAME CROSS EFFICIENCY APPROACH TO PORTFOLIO SELECTIONIn this work, we provide a new approach to portfolio selection based on game cross efficiency model. While game cross efficiency is developed as a remedy for weight multiplicity in the original cross efficiency and peer evaluation, we improve its use as a tool for portfolio selection. In fact, in our analysis we view every financial asset as a player competing for investment funds through boosting his ranking compared to his opponents. Game cross efficiency allows us to model the portfolio selection as game through considering efficiency scores as payoffs and weight selection as strategy. In this analysis, we use Data Envelopment Analysis (DEA) in a multicriteria context and employ the algorithm developed by Wu et al. (2008) to solve the Nash equilibrium rating score, which is a more reliable result for the decision maker. Furthermore, we improve on the use of cross efficiency evaluation through the development of a mean cross linear model, in which we seek to maximize the overall efficiency score of a portfolio subject to a tradeoff level with portfolio return. Finally, in addition to (average) game cross-efficiency scores, we will examine the relevant DEA literature. The main advantage accomplished by our approach is a better risk adjusted portfolio compared to benchmark index in the Euronext including; CAC40, AEX, BEL20 and PSI20 over the test period starting 2010 to 2015. We apply the proposed approach to stock portfolio selection in the Paris stock Exchange, and demonstrate that our approach can be a promising tool for stock portfolio selection by showing that the resulting portfolio yields higher risk-adjusted returns than other benchmark portfolios for a 6-year sample period from 2010 to 2015.
ttumiel / Procgen CompetitionSample efficiency and generalisation in reinforcement learning using procedural generation.
EU-ECDC / PooledPrevalenceA set of tools to help design a laboratory based prevalence study harnessing the pooling of laboratory samples to increase resource efficiency.
hkinke / Sac AeImproving Sample Efficiency in Model-Free Reinforcement Learning from Images (Yarats and al.,2020)
christinakouridi / BabygiePPO-based A2C agent with a Graph Instruction Encoder; evaluation of sample efficiency and generalisation on the BabyAI environment
stweigand97 / Model Based Pde ControlNumerical Evidence for Sample Efficiency of Model-Based over Model-Free Reinforcement Learning Control of Partial Differential Equations [ECC'24]
