explore.zoom.us/en/terms/
Zoom terms now allow training AI on user content with no opt out
Points: 1581 | Comments: 511 | Author: isodev
www.reddit.com/r/Whatcouldgowrong/comments/1m0h9cu/sliding_down_a_fireman_pole_with_no_training/
sliding down a fireman pole with no training
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arxiv.org/abs/2503.21893v2
Exponentially Weighted Instance-Aware Repeat Factor Sampling for Long-Tailed Obj…
Object detection models often struggle with class imbalance, where rare categories appear significantly less frequently than common ones. Existing sampling-based rebalancing strategies, such as Repeat Factor Sampling (RFS) and Instance-Aware Repeat F...
www.bing.com/ck/a?!&&p=549a375ee16ebdcb23accd48fc7f1b4a9ba45d1fb92a09abe0ddff6f04026b3aJmltdHM9MTc3MjQ5NjAwMA&ptn=3&ver=2&hsh=4&fclid=270bc8ff-6429-67c2-27e6-dfee65e96644&u=a1aHR0cHM6Ly9zdXBwb3J0Lm1pY3Jvc29mdC5jb20vaG9tZS9jb250YWN0P2xpbmtxdWVyeT1jb3BpbG90&ntb=1
Contact - Microsoft Support
Microsoft 365 Education How to buy for your school Educator training and development Deals for students and parents AI for education
arxiv.org/abs/2104.02251v1
Being-ahead: Benchmarking and Exploring Accelerators for Hardware-Efficient AI D…
Customized hardware accelerators have been developed to provide improved performance and efficiency for DNN inference and training. However, the existing hardware accelerators may not always be suitable for handling various DNN models as their archit...
arxiv.org/abs/2008.04933v3
PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks
Retrieving accurate 3D reconstructions of objects from the way they reflect light is a very challenging task in computer vision. Despite more than four decades since the definition of the Photometric Stereo problem, most of the literature has had lim...
www.reddit.com/r/IAmA/comments/10h5x7n/im_brett_murphy_a_propublica_reporter_who_just/
I’m Brett Murphy, a ProPublica reporter who just published a series on 911 CALL…
PROOF: https://i.redd.it/s3cnsz6sz8da1.jpg For more than a decade, a training program known as 911 call analysis and its methods have spread across the country and burrowed deep into the justice syst...
arxiv.org/abs/2405.13977v3
Improving Fairness and Mitigating MADness in Generative Models
Generative models unfairly penalize data belonging to minority classes, suffer from model autophagy disorder (MADness), and learn biased estimates of the underlying distribution parameters. Our theoretical and empirical results show that training gen...
arxiv.org/abs/2405.00662v3
No Representation, No Trust: Connecting Representation, Collapse, and Trust Issu…
Reinforcement learning (RL) is inherently rife with non-stationarity since the states and rewards the agent observes during training depend on its changing policy. Therefore, networks in deep RL must be capable of adapting to new observations and fit...
arxiv.org/abs/2310.13720v1
Eye Tracking for Tele-robotic Surgery: A Comparative Evaluation of Head-worn Sol…
Purpose: Metrics derived from eye-gaze-tracking and pupillometry show promise for cognitive load assessment, potentially enhancing training and patient safety through user-specific feedback in tele-robotic surgery. However, current eye-tracking solut...
arxiv.org/abs/1806.00630v1
DAQN: Deep Auto-encoder and Q-Network
The deep reinforcement learning method usually requires a large number of training images and executing actions to obtain sufficient results. When it is extended a real-task in the real environment with an actual robot, the method will be required mo...
arxiv.org/abs/2103.04685v1
A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels
Positive-unlabeled learning refers to the process of training a binary classifier using only positive and unlabeled data. Although unlabeled data can contain positive data, all unlabeled data are regarded as negative data in existing positive-unlabel...
arxiv.org/abs/2510.06762v2
Function regression using the forward forward training and inferring paradigm
Function regression/approximation is a fundamental application of machine learning. Neural networks (NNs) can be easily trained for function regression using a sufficient number of neurons and epochs. The forward-forward learning algorithm is a novel...
en.wikipedia.org/wiki/Customer_support
Customer support - Wikipedia
product. It includes assistance in planning, installation, training, troubleshooting, maintenance, upgrading, and disposal of a product. Regarding technology
github.com/jumpingrivers/australian-weather
jumpingrivers/australian-weather
A simple data science project exploring the weather in Australia. Used in our "Git for Me" training course. (⭐ 3)
www.reddit.com/r/technology/comments/1rcigcs/sam_altman_compares_ai_energy_use_to_the_cost_of/
Sam Altman compares AI energy use to the cost of "training" humans, says water-u…
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www.reddit.com/r/nottheonion/comments/1rbez5t/training_a_human_takes_20_years_of_food_sam/
"Training a human takes 20 years of food." Sam Altman on how much power AI consu…
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arxiv.org/abs/2512.13874v1
SAGE: Training Smart Any-Horizon Agents for Long Video Reasoning with Reinforcem…
As humans, we are natural any-horizon reasoners, i.e., we can decide whether to iteratively skim long videos or watch short ones in full when necessary for a given task. With this in mind, one would expect video reasoning models to reason flexibly ac...
arxiv.org/abs/2211.00113v1
SAGE: Saliency-Guided Mixup with Optimal Rearrangements
Data augmentation is a key element for training accurate models by reducing overfitting and improving generalization. For image classification, the most popular data augmentation techniques range from simple photometric and geometrical transformation...
arxiv.org/abs/2406.10082v3
Whisper-Flamingo: Integrating Visual Features into Whisper for Audio-Visual Spee…
Audio-Visual Speech Recognition (AVSR) uses lip-based video to improve performance in noise. Since videos are harder to obtain than audio, the video training data of AVSR models is usually limited to a few thousand hours. In contrast, speech models s...