QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks

Recent progress in computer vision-oriented neural network designs is mostly driven by capturing high-order neural interactions among inputs and features. And there emerged a variety of approaches to accomplish this, such as Transformers and its variants. However, these interactions generate a large amount of intermediate state and/or strong data dependency, leading to considerable memory consumption and computing cost, and therefore compromising the overall runtime performance. To address this challenge, we rethink the high-order interactive neural network design with a quadratic computing approach. Specifically, we propose QuadraNet -- a comprehensive model design methodology from neuron reconstruction to structural block and eventually to the overall neural network implementation. Leveraging quadratic neurons' intrinsic high-order advantages and dedicated computation optimization schemes, QuadraNet could effectively achieve optimal cognition and computation performance. Incorporating state-of-the-art hardware-aware neural architecture search and system integration techniques, QuadraNet could also be well generalized in different hardware constraint settings and deployment scenarios. The experiment shows thatQuadraNet achieves up to 1.5$\times$ throughput, 30% less memory footprint, and similar cognition performance, compared with the state-of-the-art high-order approaches.

Image Harmonization with Diffusion Model

Image composition in image editing involves merging a foreground image with a background image to create a composite. Inconsistent lighting conditions between the foreground and background often result in unrealistic composites. Image harmonization addresses this challenge by adjusting illumination and color to achieve visually appealing and consistent outputs. In this paper, we present a novel approach for image harmonization by leveraging diffusion models. We conduct a comparative analysis of two conditional diffusion models, namely Classifier-Guidance and Classifier-Free. Our focus is on addressing the challenge of adjusting illumination and color in foreground images to create visually appealing outputs that seamlessly blend with the background. Through this research, we establish a solid groundwork for future investigations in the realm of diffusion model-based image harmonization.

Distributionally Robust Optimization Efficiently Solves Offline Reinforcement Learning

Offline reinforcement learning aims to find the optimal policy from a pre-collected dataset without active exploration. This problem is faced with major challenges, such as a limited amount of data and distribution shift. Existing studies employ the principle of pessimism in face of uncertainty, and penalize rewards for less visited state-action pairs. In this paper, we directly model the uncertainty in the transition kernel using an uncertainty set, and then employ the approach of distributionally robust optimization that optimizes the worst-case performance over the uncertainty set. We first design a Hoeffding-style uncertainty set, which guarantees that the true transition kernel lies in the uncertainty set with high probability. We theoretically prove that it achieves an $\epsilon$-accuracy with a sample complexity of $\mathcal{O}\left((1-\gamma)^{-4}\epsilon^{-2}SC^{\pi^*} \right)$, where $\gamma$ is the discount factor, $C^{\pi^*}$ is the single-policy concentrability for any comparator policy $\pi^*$, and $S$ is the number of states. We further design a Bernstein-style uncertainty set, which does not necessarily guarantee the true transition kernel lies in the uncertainty set. We show an improved and near-optimal sample complexity of $\mathcal{O}\left((1-\gamma)^{-3}\epsilon^{-2}\left(SC^{\pi^*}+(\mu_{\min})^{-1}\right) \right)$, where $\mu_{\min}$ denotes the minimal non-zero entry of the behavior distribution. In addition, the computational complexity of our algorithms is the same as one of the LCB-based methods in the literature. Our results demonstrate that distributionally robust optimization method can also efficiently solve offline reinforcement learning.

A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at A Large Scale

Fairy tales are a common resource for young children to learn a language or understand how a society works. However, gender bias, e.g., stereotypical gender roles, in this literature may cause harm and skew children's world view. Instead of decades of qualitative and manual analysis of gender bias in fairy tales, we computationally analyze gender bias in a fairy tale dataset containing 624 fairy tales from 7 different cultures. We specifically examine gender difference in terms of moral foundations, which are measures of human morality, and events, which reveal human activities associated with each character. We find that the number of male characters is two times that of female characters, showing a disproportionate gender representation. Our analysis further reveal stereotypical portrayals of both male and female characters in terms of moral foundations and events. Female characters turn out more associated with care-, loyalty- and sanctity- related moral words, while male characters are more associated with fairness- and authority- related moral words. Female characters' events are often about emotion (e.g., weep), appearance (e.g., comb), household (e.g., bake), etc.; while male characters' events are more about profession (e.g., hunt), violence (e.g., destroy), justice (e.g., judge), etc. Gender bias in terms of moral foundations shows an obvious difference across cultures. For example, female characters are more associated with care and sanctity in high uncertainty-avoidance cultures which are less open to changes and unpredictability. Based on the results, we propose implications for children's literature and early literacy research.

Submission-Aware Reviewer Profiling for Reviewer Recommender System

Assigning qualified, unbiased and interested reviewers to paper submissions is vital for maintaining the integrity and quality of the academic publishing system and providing valuable reviews to authors. However, matching thousands of submissions with thousands of potential reviewers within a limited time is a daunting challenge for a conference program committee. Prior efforts based on topic modeling have suffered from losing the specific context that help define the topics in a publication or submission abstract. Moreover, in some cases, topics identified are difficult to interpret. We propose an approach that learns from each abstract published by a potential reviewer the topics studied and the explicit context in which the reviewer studied the topics. Furthermore, we contribute a new dataset for evaluating reviewer matching systems. Our experiments show a significant, consistent improvement in precision when compared with the existing methods. We also use examples to demonstrate why our recommendations are more explainable. The new approach has been deployed successfully at top-tier conferences in the last two years.

Extensible Proxy for Efficient NAS

Neural Architecture Search (NAS) has become a de facto approach in the recent trend of AutoML to design deep neural networks (DNNs). Efficient or near-zero-cost NAS proxies are further proposed to address the demanding computational issues of NAS, where each candidate architecture network only requires one iteration of backpropagation. The values obtained from the proxies are considered the predictions of architecture performance on downstream tasks. However, two significant drawbacks hinder the extended usage of Efficient NAS proxies. (1) Efficient proxies are not adaptive to various search spaces. (2) Efficient proxies are not extensible to multi-modality downstream tasks. Based on the observations, we design a Extensible proxy (Eproxy) that utilizes self-supervised, few-shot training (i.e., 10 iterations of backpropagation) which yields near-zero costs. The key component that makes Eproxy efficient is an untrainable convolution layer termed barrier layer that add the non-linearities to the optimization spaces so that the Eproxy can discriminate the performance of architectures in the early stage. Furthermore, to make Eproxy adaptive to different downstream tasks/search spaces, we propose a Discrete Proxy Search (DPS) to find the optimized training settings for Eproxy with only handful of benchmarked architectures on the target tasks. Our extensive experiments confirm the effectiveness of both Eproxy and Eproxy+DPS. Code is available at https://github.com/leeyeehoo/GenNAS-Zero.

Can Language Models Be Specific? How?

A good speaker not only needs to be correct, but also has the ability to be specific when desired, and so are language models. In this paper, we propose to measure how specific the language of pre-trained language models (PLMs) is. To achieve this, we introduce a novel approach to build a benchmark for specificity testing by forming masked token prediction tasks with prompts. For instance, given ``J. K. Rowling was born in [MASK].'', we want to test whether a more specific answer will be better filled in by PLMs, e.g., Yate instead of England. From our evaluations, we show that existing PLMs have only a slight preference for more specific answers. We identify underlying factors affecting the specificity and design two prompt-based methods to improve the specificity. Results show that the specificity of the models can be improved by the proposed methods without additional training. We believe this work can provide new insights for language modeling and encourage the research community to further explore this important but understudied problem.

Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT

We propose a novel application of prompting Pre-trained Language Models (PLMs) to generate analogies and study how to design effective prompts for two task settings: generating a source concept analogous to a given target concept (aka Analogous Concept Generation or ACG), and generating an explanation of the similarity between a given pair of target concept and source concept (aka Analogous Explanation Generation or AEG). We found that it is feasible to prompt InstructGPT to generate meaningful analogies and the best prompts tend to be precise imperative statements especially with a low temperature setting. We also systematically analyzed the sensitivity of the InstructGPT model to prompt design, temperature, and injected spelling errors, and found that the model is particularly sensitive to certain variations (e.g., questions vs. imperative statements). Further, we conducted human evaluation on 1.4k of the generated analogies and found that the quality of generations varies substantially by model size. The largest InstructGPT model can achieve human-level performance at generating meaningful analogies for a given target while there is still room for improvement on the AEG task.

HiKonv: Maximizing the Throughput of Quantized Convolution With Novel Bit-wise Management and Computation

Quantization for CNN has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data representations. There are, however, no systematic studies on how an existing full-bitwidth processing unit, such as ALU in CPUs and DSP in FPGAs, can be better utilized to deliver significantly higher computation throughput for convolution under various quantized bitwidths. In this study, we propose HiKonv, a unified solution that maximizes the throughput of convolution on a given underlying processing unit with low-bitwidth quantized data inputs through novel bit-wise management and parallel computation. We establish theoretical framework and performance models using a full-bitwidth multiplier for highly parallelized low-bitwidth convolution, and demonstrate new breakthroughs for high-performance computing in this critical domain. For example, a single 32-bit processing unit in CPU can deliver 128 binarized convolution operations (multiplications and additions) and 13 4-bit convolution operations with a single multiplication instruction, and a single 27x18 multiplier in the FPGA DSP can deliver 60, 8 or 2 convolution operations with 1, 4 or 8-bit inputs in one clock cycle. We demonstrate the effectiveness of HiKonv on both CPU and FPGA. On CPU, HiKonv outperforms the baseline implementation with 1 to 8-bit inputs and provides up to 7.6x and 1.4x performance improvements for 1-D convolution, and performs 2.74x and 3.19x over the baseline implementation for 4-bit signed and unsigned data inputs for 2-D convolution. On FPGA, HiKonv solution enables a single DSP to process multiple convolutions with a shorter processing latency. For binarized input, each DSP with HiKonv is equivalent up to 76.6 LUTs. Compared to the DAC-SDC 2020 champion model, HiKonv achieves a 2.37x throughput improvement and 2.61x DSP efficiency improvement, respectively.

Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling

Graph convolutional networks (GCNs) have recently achieved great empirical success in learning graph-structured data. To address its scalability issue due to the recursive embedding of neighboring features, graph topology sampling has been proposed to reduce the memory and computational cost of training GCNs, and it has achieved comparable test performance to those without topology sampling in many empirical studies. To the best of our knowledge, this paper provides the first theoretical justification of graph topology sampling in training (up to) three-layer GCNs for semi-supervised node classification. We formally characterize some sufficient conditions on graph topology sampling such that GCN training leads to a diminishing generalization error. Moreover, our method tackles the nonconvex interaction of weights across layers, which is under-explored in the existing theoretical analyses of GCNs. This paper characterizes the impact of graph structures and topology sampling on the generalization performance and sample complexity explicitly, and the theoretical findings are also justified through numerical experiments.