Predicting the future trajectory of a person remains a challenging problem, due to randomness and subjectivity of human movement. However, the moving patterns of human in a constrained scenario typically conform to a limited number of regularities to a certain extent, because of the scenario restrictions (e.g., floor plan, roads, and obstacles) and person-person or person-object interactivity. Thus, an individual person in this scenario should follow one of the regularities as well. In other words, a person's subsequent trajectory has likely been traveled by others. Based on this hypothesis, we propose to forecast a person's future trajectory by learning from the implicit scene regularities. We call the regularities, inherently derived from the past dynamics of the people and the environment in the scene, scene history.

Users typically engage with LLMs interactively, yet most existing benchmarks evaluate them in a static, single-turn format, posing reliability concerns in interactive scenarios. We identify a key obstacle towards reliability: LLMs are trained to answer any question, even with incomplete context or insufficient knowledge.

In many web applications, deep learning-based CTR prediction models (deep CTR models for short) are widely adopted. Traditional deep CTR models learn patterns in a static manner, i.e., the network parameters are the same across all the instances. However, such a manner can hardly characterize each of the instances which may have different underlying distributions. It actually limits the representation power of deep CTR models, leading to sub-optimal results. In this paper, we propose an efficient, effective, and universal module, named as Adaptive Parameter Generation network (APG), which can dynamically generate parameters for deep CTR models on-the-fly based on different instances. Extensive experimental evaluation results show that APG can be applied to a variety of deep CTR models and significantly improve their performance. Meanwhile, APG can reduce the time cost by 38.7% and memory usage by 96.6% compared to a regular deep CTR model. We have deployed APG in the industrial sponsored search system and achieved 3% CTR gain and 1% RPM gain respectively.

In Section 4, we require a version of Jensen's inequality generalised to (possibly) infinite-dimensional vector spaces, because our random variable takes values in H R. Note that this square norm function is indeed convex, since, for any t [0, 1] and any pair f, g H Suppose T is a real Hausdorff locally convex (possibly infinite-dimensional) linear topological space, and let C be a closed convex subset of T. Suppose (Ω, F, P) is a probability space, and V: Ω T a Pettis-integrable random variable such that V (Ω) C. Let f: C [,) be a convex, lower semi-continuous extended-real-valued function such that E We will actually apply generalised Jensen's inequality with conditional expectations, so we need the following theorem. Suppose T is a real Hausdorff locally convex (possibly infinite-dimensional) linear topological space, and let C be a closed convex subset of T. Suppose (Ω, F, P) is a probability space, and V: Ω T a Pettis-integrable random variable such that V (Ω) C. Let f: C [,) be a convex, lower semi-continuous extended-realvalued function such that E Here, (*) and (**) use the properties of conditional expectation of vector-valued random variables given in [12, pp.45-46, Properties 43 and 40 respectively]. The right-hand side is clearly E-measurable, since we have a linear operator on an E-measurable random variable. Now take the supremum of the right-hand side over Q. Then (5) tells us that E [ f(V) | E ] ( f E [ V | E ]), as required.

Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities.

On May 23, the final installment of the Mission: Impossible saga is set to come to an end with Mission: Impossible – The Final Reckoning. Known for its vicious villains, the franchise sports its Biggest Bad yet: an AI known as The Entity that's bent on wiping humans off the planet. Mashable Senior Creative Producer Mark Stetson sat down with the cast (Simon Pegg, Angela Bassett, Hayley Atwell, Pom Klementieff, and Greg Tarzan Davis) to discuss the film's themes of humanity and friendship and its exploration of the future of AI. First, Simon Pegg, who has played Benji Dunn since Mission: Impossible III -- when we first get a hint of The Entity's existence -- helped break down the origins of this Big Bad. "Yeah, I mean, the Entity was around in its nascent form a long time ago. It was a malicious code, basically, which itself evolved into what we are up against in Dead Reckoning, in The Final Reckoning. And I love the idea that McQ [Director Christopher McQuarrie] looked back into the past to see where things may have started, where the rumblings of the Entity may have begun. And further back as well, to, obviously, when Bill Donloe was exiled to Alaska."

Mathematically characterizing the implicit regularization induced by gradientbased optimization is a longstanding pursuit in the theory of deep learning. A widespread hope is that a characterization based on minimization of norms may apply, and a standard test-bed for studying this prospect is matrix factorization (matrix completion via linear neural networks). It is an open question whether norms can explain the implicit regularization in matrix factorization. The current paper resolves this open question in the negative, by proving that there exist natural matrix factorization problems on which the implicit regularization drives all norms (and quasi-norms) towards infinity. Our results suggest that, rather than perceiving the implicit regularization via norms, a potentially more useful interpretation is minimization of rank. We demonstrate empirically that this interpretation extends to a certain class of non-linear neural networks, and hypothesize that it may be key to explaining generalization in deep learning.

Mathematically characterizing the implicit regularization induced by gradientbased optimization is a longstanding pursuit in the theory of deep learning. A widespread hope is that a characterization based on minimization of norms may apply, and a standard test-bed for studying this prospect is matrix factorization (matrix completion via linear neural networks). It is an open question whether norms can explain the implicit regularization in matrix factorization. The current paper resolves this open question in the negative, by proving that there exist natural matrix factorization problems on which the implicit regularization drives all norms (and quasi-norms) towards infinity. Our results suggest that, rather than perceiving the implicit regularization via norms, a potentially more useful interpretation is minimization of rank. We demonstrate empirically that this interpretation extends to a certain class of non-linear neural networks, and hypothesize that it may be key to explaining generalization in deep learning.

We thank reviewers for their time and effort! Miscellaneous () Thank you for the positive feedback! Miscellaneous () Thank you for the feedback and support! By this they refute the prospect of norms being implicitly minimized on every convex objective. To our knowledge, very few have endorsed this far-reaching prospect.