Graph neural networks (GNNs) have emerged as a powerful tool for learning softwareengineering tasksincluding codecompletion, bugfinding,andprogram repair. The IPA-GNN can be seen either as a continuous relaxation of the RNN model or as a GNN variant more tailored to execution.

In order to build our latency prediction model, We test three types of hardware devices, NVIDIA V100, NVIDIA GTX 2080, and NVIDIA GTX 1080. Their respective properties are presented in Table 6. It shows that the server GPU V100 is the most powerful hardware device with the most processing engines (#PE). We map the operations to hardware. These split tiles are assigned to multiple PEs.

A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation Download PDF Neal Gregory Lawton, Alfy Samuel, Anoop Kumar, Daben Liu Published: 20 Aug 2025, Retrieval augmented generation (RAG) is a popular framework for question answering that is powered by two large language models (LLMs): an embedding model that retrieves context documents from a database that are relevant to a given question, and a generator model that uses the retrieved context to generate an answer to the question. Both the embedding and generator models can be fine-tuned to increase performance of a RAG pipeline on a new task, but multiple fine-tuning strategies exist with different costs and benefits. In this paper, we evaluate and compare several RAG fine-tuning strategies, including independent, joint, and two-phase fine-tuning. In our experiments, we observe that all of these strategies achieve about equal improvement in EM and F1 generation quality metrics, although they have significantly different computational costs. We conclude the optimal fine-tuning strategy to use depends on whether the training dataset includes context labels and whether a grid search over the learning rates for the embedding and generator models is required.

We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constrained to be locally linear. Our model is derived directly from an optimal control formulation in latent space, supports long-term prediction of image sequences and exhibits strong performance on a variety of complex control problems.

Responsible for approximately two million new cases and over six hundred thousand deaths in 2022 alone (Sung et al., 2021), breast cancer remains a prominent global health concern, and is expected to account nearly one-third of all newly diagnosed cancers among women in the United States (DeSantis et al., 2016). According to the most recent report from International Agency for Research on Cancer (Bray et al., 2024), it is one of the most widespread cancers diagnosed worldwide, both in the number of cases and associated deaths. Consequently, medical imaging techniques are indispensable for screening, diagnosis, and further research into the disease. Historically, the most common imaging technique for breast cancer screening is digital mammography (DM), in which a 2D x-ray projection of a compressed breast is taken. Digital breast tomosynthesis (DBT), a pseudo-3D imaging technique, has been increasingly adopted, demonstrating improved screening performance (Asbeutah et al., 2019; Sprague et al., 2023).

This 2SP has a set of continuous first-stage decisions which yield an immediate revenue. In the second stage, after a set of random variables are realized, a set of binary decisions can be made to receive further profit. In this work, we specifically consider the instance described in the example 7.3. of [Schultz et al., 1998].

We provide the details of the proposed latency-driven fast slimming in Alg. 1. Formulations of the Our major conclusions and speed analysis can be found in Sec. 3 and Figure 1. Compared to non-overlap large-kernel patch embedding (V5 in Tab. MHSA with the global receptive field is an essential contribution to model performance. By comparing V1 and V2 in Tab. 3, we can observe that the GN We explore ReLU and HardSwish (V3 and V4 in Tab. 3) in addition to GeLU We draw a conclusion that the activation function can be selected on a case-by-case basis depending on the specific hardware and compiler. In this work, we use GeLU to provide better performance than ReLU while executing faster.

Coupon distribution is a critical marketing strategy used by online platforms to boost revenue and enhance user engagement. Regrettably, existing coupon distribution strategies fall far short of effectively leveraging the complex sequential interactions between platforms and users. This critical oversight, despite the abundance of e-commerce log data, has precipitated a performance plateau. In this paper, we focus on the scene that the platforms make sequential coupon distribution decision multiple times for various users, with each user interacting with the platform repeatedly. Based on this marketing scenario, we propose a novel marketing framework, named Aligned Decision Transformer for Coupons (ADT4Coupons), to directly devise coupon distribution policy for long-term revenue boosting. ADT4Coupons enables optimized online decision-making in a variety of real-world marketing scenarios. It achieves this by seamlessly integrating three key characteristics, general scenarios, sequential modeling with more comprehensive historical data, and efficient iterative updates within a unified framework. Furthermore, empirical results on real-world industrial dataset, alongside public and synthetic datasets demonstrate the superiority of our framework.