Amazon Prime Day is back again. It wasn't that long since the last one--the retailer held its first-ever fall Prime Day sales event in 2022, and there will be another one this fall as well. But right now, the two-day event runs through July 12. We've spent hours combing through thousands of lists to find the best Prime Day deals 2023 on WIRED-tested gear, from Fire tablets to video games and Apple Watches to standing desks. Updated Wednesday, July 12: We added a bunch more deals we love and added prices and links. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. See the rest of our Phone and Tablet Deals for Prime Day here. Even with the addition of the 10th-generation iPad, we still think the ninth-generation iPad (8/10, WIRED Recommends) from 2021 is the best iPad for most people. It has the same shape and size as its predecessors, so all current accessories will work, including the first-generation Apple Pencil and Apple's Smart Keyboard. It retains the classic Home button with Touch ID plus thick borders around the 10.2-inch screen. This is the lowest price we've tracked. The Apple Pencil is one of the most useful tools you can add to the iPad. The second-gen pencil works with nearly every iPad in Apple's current lineup (except for the 9th- and 10th-gen iPad; if you have one, the first-gen Pencil is also on sale). Like a normal pencil, your lines get thicker as you press down harder. The Pencil is also great for navigating iPadOS, which has handwriting support in various search fields so you don't need to switch to the keyboard to type. It pairs and charges automatically when you stick it to the edge of the slate. Logitech's Combo Touch case is detachable, so you can ditch the keyboard when you don't need it and still have a kickstand case. It's fairly slim, with a lovely fabric texture, and the kickstand easily passes the lap test--it didn't wobble much or make the iPad fall off while you typed with it on your lap. If you want a bigger screen for travel, the iPad Mini (8/10, WIRED Recommends) has the edge over its peers. The design mimics the iPad Pro, with slim bezels around the 8.3-inch screen. Its compact size makes it the best slate to take with you everywhere. You might even be able to fit it into your cargo pants.

Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness yperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.

Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastive learning (GCL) has exhibited powerful performance in addressing the supervision label shortage issue by learning augmented user and item representations. While many of them show their effectiveness, two key questions still remain unexplored: i) Most existing GCL-based CF models are still limited by ignoring the fact that user-item interaction behaviors are often driven by diverse latent intent factors (e.g., shopping for family party, preferred color or brand of products); ii) Their introduced non-adaptive augmentation techniques are vulnerable to noisy information, which raises concerns about the model's robustness and the risk of incorporating misleading self-supervised signals. In light of these limitations, we propose a Disentangled Contrastive Collaborative Filtering framework (DCCF) to realize intent disentanglement with self-supervised augmentation in an adaptive fashion. With the learned disentangled representations with global context, our DCCF is able to not only distill finer-grained latent factors from the entangled self-supervision signals but also alleviate the augmentation-induced noise. Finally, the cross-view contrastive learning task is introduced to enable adaptive augmentation with our parameterized interaction mask generator. Experiments on various public datasets demonstrate the superiority of our method compared to existing solutions. Our model implementation is released at the link https://github.com/HKUDS/DCCF.

User Modeling plays an essential role in industry. In this field, task-agnostic approaches, which generate general-purpose representation applicable to diverse downstream user cognition tasks, is a promising direction being more valuable and economical than task-specific representation learning. With the rapid development of Internet service platforms, user behaviors have been accumulated continuously. However, existing general-purpose user representation researches have little ability for full-life cycle modeling on extremely long behavior sequences since user registration. In this study, we propose a novel framework called full- Life cycle User Representation Model (LURM) to tackle this challenge. Specifically, LURM consists of two cascaded sub-models: (I) Bag-of-Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (e.g., 10^5); (II) Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of BoI features to multiple low-dimensional user representations. Specially, SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user interests. Experiments on several benchmark datasets show that our approach outperforms state-of-the-art general-purpose representation methods.

Amazon Prime Day is back again. It wasn't that long since the last one--the retailer held its first-ever fall Prime Day sales event in 2022, and there will be another one this fall as well. But right now, the two-day event runs through July 12. We've spent hours combing through thousands of lists to find the best Prime Day deals 2023 on WIRED-tested gear, from Fire tablets to video games and Apple Watches to standing desks. Many of these deals may require a membership, including Amazon Prime, Target Circle, or Walmart Plus. Amazon Prime has a free 30-day trial if you don't already have it (set a reminder to cancel it before it automatically renews). Target Circle is free to sign up, but you'll need to save the offer to your account from the main offer page or on the actual buy page for a particular item to see the deal price at checkout. Many of Walmart's deals were restricted for a time to subscribers but are available for just about anyone right now. Updated July 11: We added the Vitamix 5200 blender, Fire Max 11 tablet, Govee Lyra smart lamp, Willow Go wearable breast pump, Google Nest Hub (2nd Gen) smart display, Lodge Dutch Oven, All-Clad stainless steel pans, and more. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. We have device deals roundups for Amazon, Google, and Apple products. Many of the best Prime Day deals are on Amazon's own hardware. Our favorite speaker for the bedroom, the Echo Dot makes a great alarm clock. It'll wake you up to your favorite playlists and read you the weather while you relax in bed. The 4th gen Echo is our favorite Alexa speaker. It offers room-filling sound with good detail and a 3.5-mm output for connecting to larger speaker systems. Our only gripe is that, despite listening to you 24/7, Alexa is still not great at picking up commands when loud music is playing. This deal includes a free LED bulb.

SHOPPING – Contains affiliated content. Products featured in this Mail Best article are selected by our shopping writers. If you make a purchase using links on this page, Dailymail.co.uk will earn an affiliate commission. Amazon's popular 2nd Generation Echo Buds, equipped with Alexa integration, are now available at a jaw-dropping 67 per cent discount, selling for just £35.99 in this late-night Lightning Deal. This early Prime Day deal has garnered attention from tech enthusiasts, particularly those comparing the Echo Buds favourably to Apple's renowned AirPods.

Like the oozing black river that gave it its name, Amazon Prime Day flows through another year. It's spreading across the internet delta too--it's now two days long, and last year there were two separate Prime Day events. This year's, ahem, prime Prime Day sales event starts now and runs through the end of July 12. We've spent hours combing through thousands of lists to find the absolute best Prime Day deals on WIRED-tested gear, from Alexa-enabled speakers and robot vacuums to laptops and tablets. Many of these deals may require a membership, including Amazon Prime, Target Circle, or Walmart Plus. Amazon Prime has a free 30-day trial if you don't already have it (set a reminder to cancel it before it automatically renews). Target Circle is free to sign up, but you'll need to save the offer to your account from the main offer page or on the actual buy page for a particular item to see the deal price at checkout. Many of Walmart's deals are in early access for subscribers but will be available for everyone later in the day. Updated July 11: We added the Colgate Hum, Motorola Razr, Niu KQi3 scooter, Victrola Re-spin, Arzopa G12 portable monitor, HyperX gaming headset and Sony WH-1000XM5. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Our favorite speaker for the bedroom, the Echo Dot makes a great alarm clock. It'll wake you up to your favorite playlists and read you the weather if you like. The 4th gen Echo is our favorite Alexa speaker. It offers room-filling sound with good detail and a 3.5-mm output for connecting to larger speaker systems. Our only gripe is that, despite listening to you 24/7, Alexa is still not great at picking up commands when loud music is playing.

SHOPPING – Contains affiliated content. Products featured in this Mail Best article are selected by our shopping writers. If you make a purchase using links on this page, Dailymail.co.uk will earn an affiliate commission. In the world of smart speakers, the Echo Dot has earned its place as a fan favourite, and now you can get your hands on the latest and greatest model at an incredible 60 per cent off thanks to this unmissable Prime Day deal. The Echo Dot (5th generation, 2022 release) is now available for just £21.99, down from its original price of £54.99 - this Prime Day deal is too good to pass up.

Like the oozing black river that gave it its name, Amazon Prime Day flows through another year. It's spreading across the internet delta too--it's now two days long and last year there were two separate Prime Day events. This year's, ahem, prime Prime Day sales event starts now and runs through the end of July 12. We've spent hours combing through thousands of lists to find the absolute best Prime Day deals on WIRED-tested gear, from Alexa-enabled speakers and robot vacuums to laptops and tablets. Note: Many of these deals may require a membership, including Amazon Prime, Target Circle, or Walmart Plus. Amazon Prime has a free 30-day trial if you don't already have it (set a reminder to cancel it before it automatically renews). Target Circle is free to sign up, but you'll need to save the offer to your account from the main offer page or on the actual buy page for a particular item to see the deal price at checkout. Many of Walmart's deals are in early access for subscribers but will be available for everyone later in the day. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Our favorite speaker for the bedroom, the Echo Dot makes a great alarm clock. It'll wake you up to your favorite playlists and read you the weather if you like. The 4th gen Echo is our favorite Alexa speaker.

Machine learning is traditionally studied at the model level: researchers measure and improve the accuracy, robustness, bias, efficiency, and other dimensions of specific models. In practice, the societal impact of machine learning is determined by the surrounding context of machine learning deployments. To capture this, we introduce ecosystem-level analysis: rather than analyzing a single model, we consider the collection of models that are deployed in a given context. For example, ecosystem-level analysis in hiring recognizes that a job candidate's outcomes are not only determined by a single hiring algorithm or firm but instead by the collective decisions of all the firms they applied to. Across three modalities (text, images, speech) and 11 datasets, we establish a clear trend: deployed machine learning is prone to systemic failure, meaning some users are exclusively misclassified by all models available. Even when individual models improve at the population level over time, we find these improvements rarely reduce the prevalence of systemic failure. Instead, the benefits of these improvements predominantly accrue to individuals who are already correctly classified by other models. In light of these trends, we consider medical imaging for dermatology where the costs of systemic failure are especially high. While traditional analyses reveal racial performance disparities for both models and humans, ecosystem-level analysis reveals new forms of racial disparity in model predictions that do not present in human predictions. These examples demonstrate ecosystem-level analysis has unique strengths for characterizing the societal impact of machine learning.