Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs).

We show an example of RTL CDFG execution (simulation) over multiple cycles in Figure 4. The input stimulus and the branches covered by the simulation are shown in Figure 5.Figure 4: Input stimulus and corresponding branches that are covered. It can potentially be used to generate constraints. Figure 6 shows the context of our solution within the industrial verification flow. Design2V ec solution inbuilt into the constrained random verification environment.

Avila is a non-image dataset. A.3 Image classification on MNIST We perform the common per-pixel zero-mean unit-variance normalization as data preprocessing for MNIST. Pretraining Hyperparameter Settings: We train neural networks for 50 epochs on MNIST for obtaining pretrained models. The batch size is set to 1 and no weight decay is used, unless we specify them otherwise. As for other optimizer hyperparameters, we apply the default settings directly.

Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs).

We show an example of RTL CDFG execution (simulation) over multiple cycles in Figure 4. The input stimulus and the branches covered by the simulation are shown in Figure 5.Figure 4: Input stimulus and corresponding branches that are covered. It can potentially be used to generate constraints. Figure 6 shows the context of our solution within the industrial verification flow. Design2V ec solution inbuilt into the constrained random verification environment.

We present NoticIA, a dataset consisting of 850 Spanish news articles featuring prominent clickbait headlines, each paired with high-quality, single-sentence generative summarizations written by humans. This task demands advanced text understanding and summarization abilities, challenging the models' capacity to infer and connect diverse pieces of information to meet the user's informational needs generated by the clickbait headline. We evaluate the Spanish text comprehension capabilities of a wide range of state-of-the-art large language models. Additionally, we use the dataset to train ClickbaitFighter, a task-specific model that achieves near-human performance in this task.

Advancements in sign language processing have been hindered by a lack of sufficient data, impeding progress in recognition, translation, and production tasks. The absence of comprehensive sign language datasets across the world's sign languages has widened the gap in this field, resulting in a few sign languages being studied more than others, making this research area extremely skewed mostly towards sign languages from high-income countries. In this work we introduce a new large and highly multilingual dataset for sign language translation: JWSign. The dataset consists of 2,530 hours of Bible translations in 98 sign languages, featuring more than 1,500 individual signers. On this dataset, we report neural machine translation experiments. Apart from bilingual baseline systems, we also train multilingual systems, including some that take into account the typological relatedness of signed or spoken languages. Our experiments highlight that multilingual systems are superior to bilingual baselines, and that in higher-resource scenarios, clustering language pairs that are related improves translation quality.