Riding a wave means surrendering to the pull. Riding a wave means surrendering to the pull. Technology is radically reshaping how we make music. As I dug deeper into this for a radio 3 documentary I began to wonder if creative organisations are right to be so upbeat about AI. Are we riding the wave or will the wave destroy us?

We consider a novel dynamic pricing and learning setting where in addition to setting prices of products in sequential rounds, the seller also ex-ante commits to'advertising schemes'. That is, in the beginning of each round the seller can decide what kind of signal they will provide to the buyer about the product's quality upon realization. Using the popular Bayesian persuasion framework to model the effect of these signals on the buyers' valuation and purchase responses, we formulate the problem of finding an optimal design of the advertising scheme along with a pricing scheme that maximizes the seller's expected revenue. Without any apriori knowledge of the buyers' demand function, our goal is to design an online algorithm that can use past purchase responses to adaptively learn the optimal pricing and advertising strategy. We study the regret of the algorithm when compared to the optimal clairvoyant price and advertising scheme.

Our model achieves simultaneous global tone manipulation and local edge detail preservation in an end-to-end manner.

Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored different approaches to training models using generated data, they generally rely on simple class-conditional prompts, which may limit the diversity of the generated data and inherit systematic biases of LLM. Thus, we investigate training data generation with diversely attributed prompts (e.g.,

Baseline Methods As standard baselines, we first consider zero-shot CLIP (ZS) and vanilla fine-tuning (FT) with contrastive loss. We construct the label map for contrastive loss by regarding all of the samples from a class as positives. A.3 Multi-modal Classification Dataset To evaluate the multi-modal representation learning under video emotional classification, CMU-MOSEI consists of three modalities textual (T), visual (V), and audio (A), and contains 23,453 Y ouTube video clips about diverse movie reviews, and each clip is annotated with ordinal labels ranging from -3 (strong negative) to 3 (strong positive). While MulT learns the joint encoder only with standard classification loss (i.e., cross-entropy loss; Metric For image-text retrieval, we adopt top-1 and top-5 recalls likewise CLIP retrieval setup. Flickr30k for zero-shot transferred image-text retrieval.