Predicting the impact of publications in science and technology has become an important research area, which is useful in various real world scenarios such as technology investment, research direction selection, and technology policymaking. Citation trajectory prediction is one of the most popular tasks in this area. Existing approaches mainly rely on mining temporal and graph data from academic articles. Some recent methods are capable of handling cold-start prediction by aggregating metadata features of new publications. However, the implicit factors causing citations and the richer information from handling temporal and attribute features still need to be explored. In this paper, we propose CTPIR, a new citation trajectory prediction framework that is able to represent the influence (the momentum of citation) of either new or existing publications using the history information of all their attributes. Our framework is composed of three modules: difference-preserved graph embedding, fine-grained influence representation, and learning-based trajectory calculation. To test the effectiveness of our framework in more situations, we collect and construct a new temporal knowledge graph dataset from the real world, named AIPatent, which stems from global patents in the field of artificial intelligence. Experiments are conducted on both the APS academic dataset and our contributed AIPatent dataset. The results demonstrate the strengths of our approach in the citation trajectory prediction task.

Review comments play an important role in the evolution of documents. For a large document, the number of review comments may become large, making it difficult for the authors to quickly grasp what the comments are about. It is important to identify the nature of the comments to identify which comments require some action on the part of document authors, along with identifying the types of these comments. In this paper, we introduce an annotated review comment dataset ReAct. The review comments are sourced from OpenReview site. We crowd-source annotations for these reviews for actionability and type of comments. We analyze the properties of the dataset and validate the quality of annotations. We release the dataset (https://github.com/gtmdotme/ReAct) to the research community as a major contribution. We also benchmark our data with standard baselines for classification tasks and analyze their performance.

Approximate computing Approximate computing (AxC) has been long accepted as a design refers to techniques that exploit the inherent error resilience alternative for efficient system implementation at the cost of relaxed of several applications to achieve improvements in efficiency (e.g., accuracy requirements. Despite the AxC research activities energy and performance) at all layers of the computing stack [60]. in various application domains, AxC thrived the past decade when For example, prior analysis on a benchmark suite of 12 recognition, it was applied in Machine Learning (ML). The by definition approximate mining and search applications showed that 83% of the runtime is notion of ML models but also the increased computational spent in tasks that are amenable to approximation [15, 60]. The origins overheads associated with ML applications-that were effectively of approximate computing (AxC) can be traced back to various mitigated by corresponding approximations-led to a perfect matching fields including computer arithmetic (floating point representation) and a fruitful synergy. AxC for AI/ML has transcended beyond [63], arithmetic units (adders [54] and multipliers [80]), digital academic prototypes. In this work, we enlighten the synergistic signal processing (filter design) [27], algorithms (approximation nature of AxC and ML and elucidate the impact of AxC in designing algorithms) [62], and networking (best-effort packet delivery) [9].

Graph coarsening is a widely used dimensionality reduction technique for approaching large-scale graph machine learning problems. Given a large graph, graph coarsening aims to learn a smaller-tractable graph while preserving the properties of the originally given graph. Graph data consist of node features and graph matrix (e.g., adjacency and Laplacian). The existing graph coarsening methods ignore the node features and rely solely on a graph matrix to simplify graphs. In this paper, we introduce a novel optimization-based framework for graph dimensionality reduction. The proposed framework lies in the unification of graph learning and dimensionality reduction. It takes both the graph matrix and the node features as the input and learns the coarsen graph matrix and the coarsen feature matrix jointly while ensuring desired properties. The proposed optimization formulation is a multi-block non-convex optimization problem, which is solved efficiently by leveraging block majorization-minimization, $\log$ determinant, Dirichlet energy, and regularization frameworks. The proposed algorithms are provably convergent and practically amenable to numerous tasks. It is also established that the learned coarsened graph is $\epsilon\in(0,1)$ similar to the original graph. Extensive experiments elucidate the efficacy of the proposed framework for real-world applications.

Figure 1: (a) Samples of stickers generated from text inputs, (b) Semantic text-guided manipulations applied to the "Original" image without using edit masks. In both cases, our model was trained without any text data. Recent text-to-image models have achieved impressive results. However, since they require large-scale datasets of text-image pairs, it is impractical to train them on new domains where data is scarce or not labeled. In this work, we propose using large-scale retrieval methods, in particular, efficient k-Nearest-Neighbors (kNN), which offers novel capabilities: (1) training a substantially small and efficient text-to-image diffusion model without any text, (2) generating out-ofdistribution images by simply swapping the retrieval database at inference time, and (3) performing text-driven local semantic manipulations while preserving object identity. To demonstrate the robustness of our method, we apply our kNN approach on two state-of-the-art diffusion backbones, and show results on several different datasets. As evaluated by human studies and automatic metrics, our method achieves state-of-the-art results compared to existing approaches that train text-to-image generation models using images only (without paired text data). Large-scale generative models have been applied successfully to image generation tasks (Gafni et al., 2022; Ramesh et al., 2021; Nichol et al., 2021; Saharia et al., 2022; Yu et al., 2022), and have shown outstanding capabilities in extending human creativity using editing and user control. However, these models face several significant challenges: (i) Large-scale paired data requirement. To achieve high-quality results, text-to-image models rely heavily on large-scale datasets of (text, image) pairs collected from the internet. Due to the requirement of paired data, these models cannot be applied to new or customized domains with only unannotated images. Training these models on highly complex distributions of natural images usually requires scaling the size of the model, data, batch-size, and training time, which makes them challenging to train and less accessible to the community.

Cross-sectional prediction is common in many domains such as healthcare, including forecasting tasks using electronic health records, where different patients form a cross-section. We focus on the task of constructing valid prediction intervals (PIs) in time series regression with a cross-section. A prediction interval is considered valid if it covers the true response with (a pre-specified) high probability. We first distinguish between two notions of validity in such a setting: cross-sectional and longitudinal. Cross-sectional validity is concerned with validity across the cross-section of the time series data, while longitudinal validity accounts for the temporal dimension. Coverage guarantees along both these dimensions are ideally desirable; however, we show that distribution-free longitudinal validity is theoretically impossible. Despite this limitation, we propose Conformal Prediction with Temporal Dependence (CPTD), a procedure that is able to maintain strict cross-sectional validity while improving longitudinal coverage. CPTD is post-hoc and light-weight, and can easily be used in conjunction with any prediction model as long as a calibration set is available. We focus on neural networks due to their ability to model complicated data such as diagnosis codes for time series regression, and perform extensive experimental validation to verify the efficacy of our approach. We find that CPTD outperforms baselines on a variety of datasets by improving longitudinal coverage and often providing more efficient (narrower) PIs.

As more practical and scalable quantum computers emerge, much attention has been focused on realizing quantum supremacy in machine learning. Existing quantum ML methods either (1) embed a classical model into a target Hamiltonian to enable quantum optimization or (2) represent a quantum model using variational quantum circuits and apply classical gradient-based optimization. The former method leverages the power of quantum optimization but only supports simple ML models, while the latter provides flexibility in model design but relies on gradient calculation, resulting in barren plateau (i.e., gradient vanishing) and frequent classical-quantum interactions. To address the limitations of existing quantum ML methods, we introduce Quark, a gradient-free quantum learning framework that optimizes quantum ML models using quantum optimization. Quark does not rely on gradient computation and therefore avoids barren plateau and frequent classical-quantum interactions. In addition, Quark can support more general ML models than prior quantum ML methods and achieves a dataset-size-independent optimization complexity. Theoretically, we prove that Quark can outperform classical gradient-based methods by reducing model query complexity for highly non-convex problems; empirically, evaluations on the Edge Detection and Tiny-MNIST tasks show that Quark can support complex ML models and significantly reduce the number of measurements needed for discovering near-optimal weights for these tasks.

When solving a real-world optimization problem we often have no information about the analytic form of the objective function. Evaluation of such black-box functions is frequently expensive in terms of time and money (Baerns and Holeňa, 2009; Lee et al., 2016; Zaefferer et al., 2016), which has been for two decades the driving force of research into surrogate modeling of black-box objective functions (Büche et al., 2005; Forrester and Keane, 2009; Jin, 2011). Given a set of observations, a surrogate model can be fitted to approximate the landscape of the black-box function. The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) by Hansen (2006), which we consider the state-of-the-art evolutionary black-box optimizer, has been frequently combined with surrogate models.

Current NAS-based semantic segmentation methods focus on accuracy improvements rather than light-weight design. In this paper, we proposed a two-stage framework to design our NAS-based RSPNet model for light-weight semantic segmentation. The first architecture search determines the inner cell structure, and the second architecture search considers exponentially growing paths to finalize the outer structure of the network. It was shown in the literature that the fusion of high- and low-resolution feature maps produces stronger representations. To find the expected macro structure without manual design, we adopt a new path-attention mechanism to efficiently search for suitable paths to fuse useful information for better segmentation. Our search for repeatable micro-structures from cells leads to a superior network architecture in semantic segmentation. In addition, we propose an RSP (recursive Stage Partial) architecture to search a light-weight design for NAS-based semantic segmentation. The proposed architecture is very efficient, simple, and effective that both the macro- and micro- structure searches can be completed in five days of computation on two V100 GPUs. The light-weight NAS architecture with only 1/4 parameter size of SoTA architectures can achieve SoTA performance on semantic segmentation on the Cityscapes dataset without using any backbones.

The discriminator Such a shift in data distribution is seen and addressed in on the other hand, attempts to discriminate between almost every field ranging from Natural Language Processing a real data sample and that from the generator. Both models are (NLP) to Object Recognition. Given labeled samples from approximated by neural networks. When trained alternatively, a source domain, there are two groups that any Domain the generator learns to produce random samples from the data Adaptation (DA) approach can be classified into, i) semisupervised distribution which are very close to the real data samples. DA: some samples in the target domain are labeled Following this, Conditional Generative Adversarial Networks or ii) unsupervised DA: none of the samples in the target (CGANs) were proposed in [8]. These networks were trained domain are labeled.