Rob Reiner's son Jake shares horrific new details from night of his parents' murders and says it is'almost impossible to process' that his brother Nick has been charged with the killings Bloodbath on the streets as millions of dogs are'massacred' by firing squad ahead of the World Cup Tucker Carlson's secret heiress sister reveals bitter feud over family fortune: He says'I don't know her'... but trove of photos tells a very different story Lesbian sex secrets of Kristi Noem's ICE leader: Ex lover claims jealous rages over men, screaming through hotel walls... and vile tight bodysuit demand Hidden cameras at NYC's live animal markets expose filthy conditions, disease risks, and brutal treatment of chickens, ducks, rabbits and sheep MAUREEN CALLAHAN: Dark indisputable Michael Jackson truths Hollywood STILL covers up. His own daughter reportedly now thinks he was a pedophile, so why's this so hard to say? Scandal after high-ranking female prison officer gave birth to twins... as shocking rumor spreads about identity of their father My senior government source has told me why these scientists may REALLY be going missing. This is so serious even the President is being kept on a'need-to-know basis': KENNEDY Former NFL quarterback Tim Tebow announces tragic news of dad's death after battle with Parkinson's in heartbreaking post Reclusive Athina Onassis, heiress to $2.7billion fortune who stepped away from public life after humiliating heartbreak, breaks cover at Barcelona Bridal Week in rare public appearance Sam's Club just launched a perk that targets Costco's biggest flaw Disappointed customers reveal the most'overrated' chain restaurants... do YOU have good taste? Woke author who boasted about shoplifting from Whole Foods flies into foul-mouthed RAGE when confronted outside her $2.2m Brooklyn brownstone Sherrone Moore's ex-mistress reveals pregnancy as she details night fired Michigan coach came to her apartment Troubling past of'father of the year' who murdered son, 11, in airport bathroom... as grieving grandpa reveals warning sign that something awful was about to happen US threatens to'review' UK claim to Falklands Islands and ban Spain from NATO as punishment for failure to back Iran War'Chemical-spraying' drones reportedly stolen from New Jersey facility sparks fears of'nightmare scenario' An alarm has erupted after 15 powerful agricultural spray drones were stolen in a suspected coordinated heist in New Jersey last month. A report from The High Side claimed the FBI is investigating the theft amid fears the machines could be used to disperse dangerous materials.

Episodic training is a core ingredient of few-shot learning to train models on tasks with limited labelled data. Despite its success, episodic training remains largely understudied, prompting us to ask the question: what is the best way to sample episodes? In this paper, we first propose a method to approximate episode sampling distributions based on their difficulty. Building on this method, we perform an extensive analysis and find that sampling uniformly over episode difficulty outperforms other sampling schemes, including curriculum and easy-/hard-mining. As the proposed sampling method is algorithm agnostic, we can leverage these insights to improve few-shot learning accuracies across many episodic training algorithms. We demonstrate the efficacy of our method across popular few-shot learning datasets, algorithms, network architectures, and protocols.

Although BERT-style encoder models are heavily used in NLP research, many researchers do not pretrain their own BERTs from scratch due to the high cost of training. In the past half-decade since BERT first rose to prominence, many advances have been made with other transformer architectures and training configurations that have yet to be systematically incorporated into BERT. Here, we introduce MosaicBERT, a BERT-style encoder architecture and training recipe that is empirically optimized for fast pretraining. This efficient architecture incorporates FlashAttention, Attention with Linear Biases (ALiBi), Gated Linear Units (GLU), a module to dynamically remove padded tokens, and low precision LayerNorm into the classic transformer encoder block. The training recipe includes a 30% masking ratio for the Masked Language Modeling (MLM) objective, bfloat16 precision, and vocabulary size optimized for GPU throughput, in addition to best-practices from RoBERTa and other encoder models. When pretrained from scratch on the C4 dataset, this base model achieves a downstream average GLUE (dev) score of 79.6 in 1.13 hours on 8 A100 80 GBGPUs at a cost of roughly $20. We plot extensive accuracy vs. pretraining speed Pareto curves and show that MosaicBERT base and large are consistently Pareto optimal when compared to a competitive BERT base and large. This empirical speed up in pretraining enables researchers and engineers to pretrain custom BERT-style models at low cost instead of finetune on existing generic models.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

Sequential recommender systems (SRSs) are typically trained to predict the next item as the target given its preceding (and succeeding) items as the input. Such a paradigm assumes that every input-target pair is reliable for training. However, users can be induced to click on items that are inconsistent with their true preferences, resulting in unreliable instances, i.e., mismatched input-target pairs. Current studies on mitigating this issue suffer from two limitations: (i) they discriminate instance reliability according to models trained with unreliable data, yet without theoretical guarantees that such a seemingly contradictory solution can be effective; and (ii) most methods can only tackle either unreliable input or targets but fail to handle both simultaneously. To fill the gap, we theoretically unveil the relationship between SRS predictions and instance reliability, whereby two error-bounded strategies are proposed to rectify unreliable targets and input, respectively. On this basis, we devise a model-agnostic Bidirectional Data Rectification (BirDRec) framework, which can be flexibly implemented with most existing SRSs for robust training against unreliable data. Additionally, a rectification sampling strategy is devised and a self-ensemble mechanism is adopted to reduce the (time and space) complexity of BirDRec. Extensive experiments on four real-world datasets verify the generality, effectiveness, and efficiency of our proposed BirDRec.

A shrinking market is a ubiquitous challenge faced by various industries. In this paper we formulate the first formal model of shrinking markets in multi-item settings, and study how mechanism design and machine learning can help preserve revenue in an uncertain, shrinking market. Via a sample-based learning mechanism, we prove the first guarantees on how much revenue can be preserved by truthful multi-item, multi-bidder auctions (for limited supply) when only a random unknown fraction of the population participates in the market. We first present a general reduction that converts any sufficiently rich auction class into a randomized auction robust to market shrinkage. Our main technique is a novel combinatorial construction called a winner diagram that concisely represents all possible executions of an auction on an uncertain set of bidders. Via a probabilistic analysis of winner diagrams, we derive a general possibility result: a sufficiently rich class of auctions always contains an auction that is robust to market shrinkage and market uncertainty. Our result has applications to important practically-constrained settings such as auctions with a limited number of winners. We then show how to efficiently learn an auction that is robust to market shrinkage by leveraging practically-efficient routines for solving the winner determination problem.