The efficient discovery of academic literature is critical for research progress, yet many researchers have difficulties in finding literature. This work proposes InstInfo: a novel just-in-time literature recommendation system for presentations. InstInfo transcribes audio in real-time and recommends literature according to the ideas being discussed, thereby helping researchers ground presentations in academic literature while saving them the time of having to manually search. Informal usability studies show that InstInfo is easy to use and that researchers find value in the recommendations. InstInfo can be accessed at https://instinfo.com.

Makeup transfer involves transferring makeup from a reference image to a target image while maintaining the target’s identity. Existing methods, which use Generative Adversarial Networks, often transfer not just makeup but also the reference image’s skin tone. This limits their use to similar skin tones and introduces bias. Our solution introduces a skin tone-robust makeup embedding achieved by augmenting the reference image with varied skin tones. Using Graph Neural Networks, we establish connections between target, reference, and augmented images to create this robust representation that preserves the target’s skin tone. In a user study, our approach outperformed other methods 66% of the time, showcasing its resilience to skin tone variations.

Autoregressive models using Transformers have emerged as the dominant approach for music generation with the goal of synthesizing minute-long compositions that exhibit large-scale musical structure. These models are commonly trained by minimizing the negative log-likelihood (NLL) of the observed sequence in an autoregressive manner. Unfortunately, the quality of samples from these models tends to degrade significantly for long sequences, a phenomenon attributed to exposure bias. Fortunately, we are able to detect these failures with classifiers trained to distinguish between real and sampled sequences, an observation that motivates our exploration of adversarial losses to complement the NLL objective. We use a pre-trained Span-BERT model for the discriminator of the GAN, which in our experiments helped with training stability. We use the Gumbel-Softmax trick to obtain a differentiable approximation of the sampling process. This makes discrete sequences amenable to optimization in GANs. In addition, we break the sequences into smaller chunks to ensure that we stay within a given memory budget. We demonstrate via human evaluations and a new discriminative metric that the music generated by our approach outperforms a baseline trained with likelihood maximization, the state-of-the-art Music Transformer, and other GANs used for sequence generation. 57% of people prefer music generated via our approach while 43% prefer Music Transformer.