Although the Cannes Film Festival is the world's most prestigious movie showcase, its spotlight rarely falls on nonfiction film. Years go by without a single documentary competing for its biggest honor, the Palme d'Or, and there is no separate documentary prize. Juliette Binoche, the president of this year's jury, devoted part of her opening-night remarks to Fatma Hassona, the Palestinian photojournalist who was killed in an Israeli airstrike the day after it was announced that her documentary Put Your Soul on Your Hand and Walk would be premiering at Cannes. But the film itself was slotted into a low-profile sidebar devoted to independent productions. The festival did, however, roll out the red carpet for The Six Billion Dollar Man, Eugene Jarecki's portrait of WikiLeaks founder Julian Assange, which premiered out of competition on Wednesday evening.

Breakthroughs, discoveries, and DIY tips sent every weekday. Magnus Carlsen is an undisputed titan in the world of chess. In 2011 at the age of 19, the Swedish grandmaster became the youngest person to ever top the International Chess Federation (FIDE) world rankings--a position he's occupied ever since. Carlsen holds the record for the highest official rating level in history, and currently trails only Gary Kasparov for the longest time spent as the sport's highest ranking player. So what would it take for the everyday chess enthusiast to give him a run for his money?

We list them in Table A.2. Running a large number of algorithm-hyperparameter pairs many times is very computationally expensive. In order to save time and resources, we leverage the fact that multiple approaches can share resources. We describe how we compute the numbers for each approach as follows: For each offline RL dataset in Sepsis, TutorBot, Robomimic, and D4RL, we produce the following partitions (we refer to this as the "partition generation procedure"): 1. 2-fold CV split (2 partitions consisted of (S

Vision-language models (VLMs) have made significant progress in recent visualquestion-answering (VQA) benchmarks that evaluate complex visio-linguistic reasoning. However, are these models truly effective? In this work, we show that VLMs still struggle with natural images and questions that humans can easily answer, which we term natural adversarial samples. We also find it surprisingly easy to generate these VQA samples from natural image-text corpora using offthe-shelf models like CLIP and ChatGPT. We propose a semi-automated approach to collect a new benchmark, NaturalBench, for reliably evaluating VLMs with 10,000 human-verified VQA samples.

President Donald Trump evoked Elon Musk during his Oval Office meeting with South Africa's president on Wednesday, during talks about the ongoing attacks white farmers in the country are facing. Trump went back and forth with President Cyril Ramaphosa over whether what is occurring in South Africa is indeed a "genocide" against white farmers. At one point, during the conversation, a reporter asked Trump how the United States and South Africa might be able to improve their relations. The president said that relations with South Africa are an important matter to him, noting he has several personal friends who are from there, including professional golfers Ernie Els and Retief Goosen, who were present at Tuesday's meeting, and Elon Musk. President Donald Trump and Elon Musk attend a UFC 309 at Madison Square Garden last November. Unprompted, Trump added that while Musk may be a South African native, he doesn't want to "get [him] involved" in the ongoing foreign diplomacy matters that played out during Tuesday's meeting.

OpenAI is officially getting into the hardware business. In a video posted to X on Wednesday, OpenAI CEO Sam Altman and former Apple designer Jony Ive, who worked on flagship products like the iPhone, revealed a partnership to create the next generation of AI-enabled devices. Also: I tried Google's XR glasses and they already beat my Meta Ray-Bans in 3 ways The AI software company announced it is merging with io, an under-the-radar startup focused on AI devices that Ive founded a year ago alongside several partners. In the video, Altman and Ive say they have been "quietly" collaborating for two years. As part of the deal, Ive and those at his design firm, LoveFrom, will remain independent but will take on creative roles at OpenAI.

Zero-shot reinforcement learning (RL) promises to provide agents that can perform any task in an environment after an offline, reward-free pre-training phase. Methods leveraging successor measures and successor features have shown strong performance in this setting, but require access to large heterogenous datasets for pre-training which cannot be expected for most real problems. Here, we explore how the performance of zero-shot RL methods degrades when trained on small homogeneous datasets, and propose fixes inspired by conservatism, a well-established feature of performant single-task offline RL algorithms. We evaluate our proposals across various datasets, domains and tasks, and show that conservative zero-shot RL algorithms outperform their non-conservative counterparts on low quality datasets, and perform no worse on high quality datasets. Somewhat surprisingly, our proposals also outperform baselines that get to see the task during training.

We begin by formally defining multihead self-attention and Transformer. Our definition is equivalent to Vaswani et al. (2017) [68], except we omit layer normalization for simplicity as in [81, 23, 34]. Consequently, each equivalence class γ in Definition 3 is a distinct set of all order-l multi-indices having a specific equality pattern. Now, for each equivalence class, we define the corresponding basis tensor as follows: Definition 4. I. Given a set of features X R Proof of Lemma 1 (Section 3.3) To prove Lemma 1, we need to show that each basis tensor B Here, our key idea is to break down the inclusion test (i, j) µ into equivalent but simpler Boolean tests that can be implemented in self-attention (Eq. To achieve this, we show some supplementary Lemmas.

Recent Convolutional Neural Networks (CNNs) have achieved significant success by stacking multiple convolutional blocks, named procedures in this paper, to extract semantic features. However, they use the same procedure sequence for all inputs, regardless of the intermediate features.

This section provides more details on the augmentation process of Figure 1. For Image Filtering (IF), s equals to 1.5, so the image is blurred by convolving with K = 1.5 G3+ Testing sets are not involved in our augmentation search process. ImageNet [2] is a challenging large scale dataset, containing about 1.28 million training The testing set is not used. Mean values and standard deviations are reported. The hyperparameters for re-training used in this paper are listed in Tab.