Recent advances in neural architecture search inspire many channel number search algorithms (CNS) for convolutional neural networks. To improve searching efficiency, parameter sharing is widely applied, which reuses parameters among different channel configurations. Nevertheless, it is unclear how parameter sharing affects the searching process. In this paper, we aim at providing a better understanding and exploitation of parameter sharing for CNS. Specifically, we propose affine parameter sharing (APS) as a general formulation to unify and quantitatively analyze existing channel search algorithms. It is found that with parameter sharing, weight updates of one architecture can simultaneously benefit other candidates. However, it also results in less confidence in choosing good architectures. We thus propose a new strategy of parameter sharing towards a better balance between training efficiency and architecture discrimination. Extensive analysis and experiments demonstrate the superiority of the proposed strategy in channel configuration against many state-of-the-art counterparts on benchmark datasets.

AI and creators mix much like oil and vinegar -- not at all unless you use a very specific technique (whisking) for a very specific purpose (making salad dressing). For Darren Aronofsky, the director behind Requiem for a Dream, The Whale, and Black Swan, that technique involves using Google DeepMind's research team and three filmmakers to produce short films that embrace new technology and storytelling. The partnership between Aronofsky's venture Primordial Soup and Google DeepMind will create frameworks for AI's role in filmmaking in an effort to prioritize artists in the conversation. It was announced during Tuesday's Google I/O, the company's annual developer conference. "Filmmaking has always been driven by technology," Aronofsky said in a press release.

When I was a child, computers were a fixture in my home, from the giant Atari on which I learned my ABCs, to the Commodore Amiga that my dad used for his videography business, to the PC towers that facilitated my first forays onto the internet. But tech was still a niche hobby back then. Even in college in the late 1990s and early 2000s, many of my friends got by just fine without computers. For people in college now--namely, my students--things are decidedly different. Gadgets are everywhere, and are increasingly designed to insert themselves into every aspect of our consciousness, colonizing every spare moment of our time and attention.

Inverse molecular design with diffusion models holds great potential for advancements in material and drug discovery. Despite success in unconditional molecular generation, integrating multiple properties such as synthetic score and gas permeability as condition constraints into diffusion models remains unexplored. We present the Graph Diffusion Transformer (Graph DiT) for multi-conditional molecular generation. Graph DiT integrates an encoder to learn numerical and categorical property representations with the Transformer-based denoiser. Unlike previous graph diffusion models that add noise separately on the atoms and bonds in the forward diffusion process, Graph DiT is trained with a novel graph-dependent noise model for accurate estimation of graph-related noise in molecules.

The Google I/O keynote took place on Tuesday, and the company took the stage to unveil new features across all of its product offerings. Of course, this included AI upgrades to the Google Workspace suite of applications, which millions of users rely on every day to get their work done, including Google Docs, Meet, Slides, Gmail, and Vids. Also: Google's popular AI tool gets its own Android app - how to use NotebookLM on your phone The features unveiled this year focused on practicality. They embed AI features into the Google apps you already use every day to speed up your daily workflow by performing tedious and time-consuming tasks, such as cleaning out your inbox. Everyone can relate to being bombarded with emails.

This year, Google I/O 2025 had one focus: Artificial intelligence. We've already covered all of the biggest news to come out of the annual developers conference: a new AI video generation tool called Flow. Yet over nearly two hours of Google leaders talking about AI, one word we didn't hear was "hallucination". Hallucinations remain one of the most stubborn and concerning problems with AI models. The term refers to invented facts and inaccuracies that large-language models "hallucinate" in their replies.

Google Gemini already offers a host of useful capabilities. From generating text and creating images to live conversations, deep research, and analyzing files, Google's AI has proven itself a strong contender in the AI field. At Google I/O 2025 on Tuesday, the company revealed a slew of new and improved features now available with its AI assistant. First up are two new Google AI subscriptions that offer more features but naturally come with their own price tags. The first plan is known as Google AI Pro, which is actually the same AI Premium plan that's been around for a while just with a new name.

At Google I/O, the company unveiled a major push to infuse AI deeper into Gmail. The tech giant is promising users that the change will translate to less time spent writing emails and more time doing literally anything else. With the help of Gemini, Google's flagship AI model, Gmail is moving from a helpful assistant to a full-on writing partner, scheduler, and inbox manager. Here are all the new Gemini-powered features coming this summer to Gmail. Google's first big update is personalized smart replies. Unlike the canned one-liners we're used to ("Sounds good," "Thanks!"), this new system draws context from your inbox and Google Drive.