Kernel survival analysis models estimate individual survival distributions with the help of a kernel function, which measures the similarity between any two data points. Such a kernel function can be learned using deep kernel survival models. In this paper, we present a new deep kernel survival model called a survival kernet, which scales to large datasets in a manner that is amenable to model interpretation and also theoretical analysis. Specifically, the training data are partitioned into clusters based on a recently developed training set compression scheme for classification and regression called kernel netting that we extend to the survival analysis setting. At test time, each data point is represented as a weighted combination of these clusters, and each such cluster can be visualized. For a special case of survival kernets, we establish a finite-sample error bound on predicted survival distributions that is, up to a log factor, optimal. Whereas scalability at test time is achieved using the aforementioned kernel netting compression strategy, scalability during training is achieved by a warm-start procedure based on tree ensembles such as XGBoost and a heuristic approach to accelerating neural architecture search. On four standard survival analysis datasets of varying sizes (up to roughly 3 million data points), we show that survival kernets are highly competitive compared to various baselines tested in terms of time-dependent concordance index. Our code is available at: https://github.com/georgehc/survival-kernets

While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this work has focused on images. In a similar spirit, we view recently proposed neural video coding algorithms through the lens of deep autoregressive and latent variable modeling. We present these codecs as instances of a generalized stochastic temporal autoregressive transform, and propose new avenues for further improvements inspired by normalizing flows and structured priors. We propose several architectures that yield state-of-the-art video compression performance on high-resolution video and discuss their tradeoffs and ablations. In particular, we propose (i) improved temporal autoregressive transforms, (ii) improved entropy models with structured and temporal dependencies, and (iii) variable bitrate versions of our algorithms. Since our improvements are compatible with a large class of existing models, we provide further evidence that the generative modeling viewpoint can advance the neural video coding field.

Ben Goertzel said the sky's'not even the limit' when it comes to the potential impact of artificial general intelligence. A panel of robots told reporters in Switzerland Friday that they could be more efficient leaders than human beings, among other statements. The nine artificial intelligence-enabled humanoid social robots also explained at a Geneva conference center that they wouldn't take anyone's jobs or stage a rebellion. Conference organizers at the United Nations-driven AI for Good Global Summit did not specify to what extent their responses were scripted or programmed. Some of the robots are capable of producing preprogrammed responses and the United Nations Development Program's first robot innovation ambassador, Sophia, sometimes relies on responses scripted by a team of writers at Hanson Robotics.

The proliferation of discussion about fatherhood in Singapore attests to its significance, indicating the need for an exploration of how fatherhood is framed, aiding policy-making around fatherhood in Singapore. Sound and holistic policy around fatherhood in Singapore may reduce stigma and apprehension around being a parent, critical to improving the nation's flagging birth rate. We analyzed 15,705 articles and 56,221 posts to study how fatherhood is framed in Singapore across a range of online platforms (news outlets, parenting forums, Twitter). We used NLP techniques to understand these differences. While fatherhood was framed in a range of ways on the Singaporean online environment, it did not seem that fathers were framed as central to the Singaporean family unit. A strength of our work is how the different techniques we have applied validate each other.

Robust explanations of machine learning models are critical to establish human trust in the models. Due to limited cognition capability, most humans can only interpret the top few salient features. It is critical to make top salient features robust to adversarial attacks, especially those against the more vulnerable gradient-based explanations. Existing defense measures robustness using $\ell_p$-norms, which have weaker protection power. We define explanation thickness for measuring salient features ranking stability, and derive tractable surrogate bounds of the thickness to design the \textit{R2ET} algorithm to efficiently maximize the thickness and anchor top salient features. Theoretically, we prove a connection between R2ET and adversarial training. Experiments with a wide spectrum of network architectures and data modalities, including brain networks, demonstrate that R2ET attains higher explanation robustness under stealthy attacks while retaining accuracy.

Most existing cross-lingual summarization (CLS) work constructs CLS corpora by simply and directly translating pre-annotated summaries from one language to another, which can contain errors from both summarization and translation processes. To address this issue, we propose ConvSumX, a cross-lingual conversation summarization benchmark, through a new annotation schema that explicitly considers source input context. ConvSumX consists of 2 sub-tasks under different real-world scenarios, with each covering 3 language directions. We conduct thorough analysis on ConvSumX and 3 widely-used manually annotated CLS corpora and empirically find that ConvSumX is more faithful towards input text. Additionally, based on the same intuition, we propose a 2-Step method, which takes both conversation and summary as input to simulate human annotation process. Experimental results show that 2-Step method surpasses strong baselines on ConvSumX under both automatic and human evaluation. Analysis shows that both source input text and summary are crucial for modeling cross-lingual summaries.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A drone strike believed to have been carried out by the U.S.-led coalition in northern Syria Friday killed one man with Islamic State links and wounded a passerby, a paramedic group and an opposition war monitor said. The Syrian Civil Defense, also known as the White Helmets, said the man was killed while riding a motorcycle. It added that a passerby was also wounded.

Subscribe now to get Fox News First in your email. And here's what you need to know to start your day ... 100 DAYS - Today marks 100 days since WSJ reporter Evan Gershkovich was detained by Russia. Journalism is not a crime, and we will not rest until Evan is released. BACK IN COURT – OnlyFans model Courtney Clenney, held in prison without bail for the fatal stabbing of her live-in boyfriend Christian Obumseli in April 2022, returns to a Florida courtroom Friday. DESPERATE DODGING - Experts astonished White House invokes Hatch Act to avoid Hunter Biden cocaine question.

A PAC affiliated with Miami Mayor Francis Suarez's presidential campaign has rolled out an AI version of the candidate to answer voter questions. A Republican 2024 candidate is deploying a digital doppelganger created with artificial intelligence, and his political allies are hoping the clone could be a first-of-his-kind "campaign surrogate" that could one day even do media interviews. A fundraising group affiliated with Miami Mayor Francis Suarez's presidential campaign has rolled out an audio and video lookalike of the conservative leader that answers voter questions about his political stances. "We wanted to do something that was going to set him apart from the rest of the crew running for president," SOS America PAC spokesman Chapin Fay told Fox News Digital. "You know, we have to. All the candidates are trying to find their lane and differentiate themselves, and I think this is one way that we can do that for Mayor Suarez."

Hall of Fame tennis coach Rick Macci weighs in on how fans will react to a computer commentator instead of a human one on'Fox & Friends.' Over the past 150 years or so the predictive power of science fiction has been remarkably prescient about myriad advancements made by humanity. Millions of Americans today walk about with a "Dick Tracy" style "wrist radio," known now as a smartwatch and spaceships and space stations dot the darkness beyond our planet. Last week the FAA even cleared the way for testing a flying car. BIDEN ADMIN, DEMS TRYING TO MAKE AI'WOKE': REPORT Sci-fi has also missed the mark now and then.