OpenRaiser/Intern-Atlas huggingface dataset:
Accelerating Neural Network Training: A Brief Review
Venue: arXiv cs.LG 2023
Year: 2023

The process of training a deep neural network is characterized by significant time requirements and associated costs. Although researchers have made considerable progress in this area, further work is still required due to resource constraints. This study examines innovative approaches to expedite the training process of deep neural networks (DNN), with specific emphasis on three state-of-the-art models such as ResNet50, Vision Transformer (ViT), and EfficientNet. The research utilizes sophisticated methodologies, including Gradient Accumulation (GA), Automatic Mixed Precision (AMP), and Pin Memory (PM), in order to optimize performance and accelerate the training procedure. The study examines the effects of these methodologies on the DNN models discussed earlier, assessing their efficacy with regard to training rate and computational efficacy. The study showcases the efficacy of including GA as a strategic approach, resulting in a noteworthy decrease in the duration required for training. This enables the models to converge at a faster pace. The utilization of AMP enhances the speed of computations by taking advantage of the advantages offered by lower precision arithmetic while maintaining the correctness of the model. Furthermore, this study investigates the application of Pin Memory as a strategy to enhance the efficiency of data transmission between the central processing unit and the graphics processing unit, thereby offering a promising opportunity for enhancing overall performance. The experimental findings demonstrate that the combination of these sophisticated methodologies significantly accelerates the training of DNNs, offering vital insights for experts seeking to improve the effectiveness of deep learning processes.
Deep Metric Learning for Computer Vision: A Brief Overview
Venue: arXiv cs.CV 2023
Year: 2023

Objective functions that optimize deep neural networks play a vital role in creating an enhanced feature representation of the input data. Although cross-entropy-based loss formulations have been extensively used in a variety of supervised deep-learning applications, these methods tend to be less adequate when there is large intra-class variance and low inter-class variance in input data distribution. Deep Metric Learning seeks to develop methods that aim to measure the similarity between data samples by learning a representation function that maps these data samples into a representative embedding space. It leverages carefully designed sampling strategies and loss functions that aid in optimizing the generation of a discriminative embedding space even for distributions having low inter-class and high intra-class variances. In this chapter, we will provide an overview of recent progress in this area and discuss state-of-the-art Deep Metric Learning approaches.
Understanding Representations Pretrained with Auxiliary Losses for Embodied Agent Planning
Venue: arXiv cs.RO 2023
Year: 2023

Pretrained representations from large-scale vision models have boosted the performance of downstream embodied policy learning. We look to understand whether additional self-supervised pretraining on exploration trajectories can build on these general-purpose visual representations to better support embodied planning in realistic environments. We evaluated four common auxiliary losses in embodied AI, two hindsight-based losses, and a standard imitation learning loss, by pretraining the agent's visual compression module and state belief representations with each objective and using CLIP as a representative visual backbone. The learned representations are then frozen for downstream multi-step evaluation on two goal-directed tasks. Surprisingly, we find that imitation learning on these exploration trajectories out-performs all other auxiliary losses even despite the exploration trajectories being dissimilar from the downstream tasks. This suggests that imitation of exploration may be ''all you need'' for building powerful planning representations. Additionally, we find that popular auxiliary losses can benefit from simple modifications to improve their support for downstream planning ability.
Finding Paths for Explainable MOOC Recommendation: A Learner Perspective
Venue: arXiv cs.IR 2023
Year: 2023

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners and courses. While these enriched representations allow more accurate and personalized recommendations, explainability remains a significant challenge which is especially problematic for certain domains with significant impact such as education and online learning. Recently, a novel class of recommender systems that uses reinforcement learning and graph reasoning over KGs has been proposed to generate explainable recommendations in the form of paths over a KG. Despite their accuracy and interpretability on e-commerce datasets, these approaches have scarcely been applied to the educational domain and their use in practice has not been studied. In this work, we propose an explainable recommendation system for MOOCs that uses graph reasoning. To validate the practical implications of our approach, we conducted a user study examining user perceptions of our new explainable recommendations. We demonstrate the generalizability of our approach by conducting experiments on two educational datasets: COCO and Xuetang.
Look Before You Leap: A Universal Emergent Decomposition of Retrieval Tasks in Language Models
Venue: arXiv cs.IR 2023
Year: 2023

When solving challenging problems, language models (LMs) are able to identify relevant information from long and complicated contexts. To study how LMs solve retrieval tasks in diverse situations, we introduce ORION, a collection of structured retrieval tasks spanning six domains, from text understanding to coding. Each task in ORION can be represented abstractly by a request (e.g. a question) that retrieves an attribute (e.g. the character name) from a context (e.g. a story). We apply causal analysis on 18 open-source language models with sizes ranging from 125 million to 70 billion parameters. We find that LMs internally decompose retrieval tasks in a modular way: middle layers at the last token position process the request, while late layers retrieve the correct entity from the context. After causally enforcing this decomposition, models are still able to solve the original task, preserving 70% of the original correct token probability in 98 of the 106 studied model-task pairs. We connect our macroscopic decomposition with a microscopic description by performing a fine-grained case study of a question-answering task on Pythia-2.8b. Building on our high-level understanding, we demonstrate a proof of concept application for scalable internal oversight of LMs to mitigate prompt-injection while requiring human supervision on only a single input. Our solution improves accuracy drastically (from 15.5% to 97.5% on Pythia-12b). This work presents evidence of a universal emergent modular processing of tasks across varied domains and models and is a pioneering effort in applying interpretability for scalable internal oversight of LMs.
Gaussian Process-Based Learning Control of Underactuated Balance Robots with an External and Internal Convertible Modeling Structure
Venue: arXiv cs.RO 2023
Year: 2023

External and internal convertible (EIC) form-based motion control is one of the effective designs of simultaneously trajectory tracking and balance for underactuated balance robots. Under certain conditions, the EIC-based control design however leads to uncontrolled robot motion. We present a Gaussian process (GP)-based data-driven learning control for underactuated balance robots with the EIC modeling structure. Two GP-based learning controllers are presented by using the EIC structure property. The partial EIC (PEIC)-based control design partitions the robotic dynamics into a fully actuated subsystem and one reduced-order underactuated system. The null-space EIC (NEIC)-based control compensates for the uncontrolled motion in a subspace, while the other closed-loop dynamics are not affected. Under the PEIC- and NEIC-based, the tracking and balance tasks are guaranteed and convergence rate and bounded errors are achieved without causing any uncontrolled motion by the original EIC-based control. We validate the results and demonstrate the GP-based learning control design performance using two inverted pendulum platforms.
Low-resource classification of mobility functioning information in clinical sentences using large language models
Venue: arXiv cs.CL 2023
Year: 2023

Objective: Function is increasingly recognized as an important indicator of whole-person health. This study evaluates the ability of publicly available large language models (LLMs) to accurately identify the presence of functioning information from clinical notes. We explore various strategies to improve the performance on this task. Materials and Methods: We collect a balanced binary classification dataset of 1000 sentences from the Mobility NER dataset, which was curated from n2c2 clinical notes. For evaluation, we construct zero-shot and few-shot prompts to query the LLMs whether a given sentence contains mobility functioning information. Two sampling techniques, random sampling and k-nearest neighbor (kNN)-based sampling, are used to select the few-shot examples. Furthermore, we apply a parameter-efficient prompt-based fine-tuning method to the LLMs and evaluate their performance under various training settings. Results: Flan-T5-xxl outperforms all other models in both zero-shot and few-shot settings, achieving a F1 score of 0.865 with a single demonstrative example selected by kNN sampling. In prompt-based fine-tuning experiments, this foundation model also demonstrates superior performance across all low-resource settings, particularly achieving an impressive F1 score of 0.922 using the full training dataset. The smaller model, Flan-T5-xl, requires fine-tuning with only 2.3M additional parameters to achieve comparable performance to the fully fine-tuned Gatortron-base model, both surpassing 0.9 F1 score. Conclusion: Open-source instruction-tuned LLMs demonstrate impressive in-context learning capability in the mobility functioning classification task. The performance of these models can be further improved by continuing fine-tuning on a task-specific dataset.
Catwalk: A Unified Language Model Evaluation Framework for Many Datasets
Venue: arXiv cs.CL 2023
Year: 2023

The success of large language models has shifted the evaluation paradigms in natural language processing (NLP). The community's interest has drifted towards comparing NLP models across many tasks, domains, and datasets, often at an extreme scale. This imposes new engineering challenges: efforts in constructing datasets and models have been fragmented, and their formats and interfaces are incompatible. As a result, it often takes extensive (re)implementation efforts to make fair and controlled comparisons at scale. Catwalk aims to address these issues. Catwalk provides a unified interface to a broad range of existing NLP datasets and models, ranging from both canonical supervised training and fine-tuning, to more modern paradigms like in-context learning. Its carefully-designed abstractions allow for easy extensions to many others. Catwalk substantially lowers the barriers to conducting controlled experiments at scale. For example, we finetuned and evaluated over 64 models on over 86 datasets with a single command, without writing any code. Maintained by the AllenNLP team at the Allen Institute for Artificial Intelligence (AI2), Catwalk is an ongoing open-source effort: https://github.com/allenai/catwalk.
Painterly Image Harmonization by Learning from Painterly Objects
Venue: arXiv cs.CV 2023
Year: 2023

Given a composite image with photographic object and painterly background, painterly image harmonization targets at stylizing the composite object to be compatible with the background. Despite the competitive performance of existing painterly harmonization works, they did not fully leverage the painterly objects in artistic paintings. In this work, we explore learning from painterly objects for painterly image harmonization. In particular, we learn a mapping from background style and object information to object style based on painterly objects in artistic paintings. With the learnt mapping, we can hallucinate the target style of composite object, which is used to harmonize encoder feature maps to produce the harmonized image. Extensive experiments on the benchmark dataset demonstrate the effectiveness of our proposed method.
Implementing A Middleware API for Facilitating Heterogeneous IoT Device Communication Protocols and Data Retrieval
Venue: arXiv cs.NI 2023
Year: 2023

Currently, there are over 14 billion IoT devices [7], and with many devices come many protocols, the main ones being MQTT and CoAP. We are interested in connecting the many diverse IoT devices to the cloud. To do so, we use the middleware architecture proposed by article [8] in which a device, called the middleware, acts as the middleman between the various IoT networks and the cloud. Since IoT devices typically operate in real-time, performance is of great concern. Therefore, we conducted a simulation to measure the data latency of using middleware and the overall fairness between different IoT networks. Our simulation had an MQTT and a CoAP network interacting with the middleware. The simulation results showed that CoAP always had a lower travel time than MQTT, mainly because CoAP is a more lightweight protocol. However, we also found that MQTT had slightly more throughput, which was unexpected since we initially thought that CoAP would have had higher throughput. We have shown that analyzing data via a middleware device is possible and that there are potential directions to explore, such as evaluating different Quality of Service Algorithms in the context of having a middleware device.
Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classification
Venue: arXiv cs.LG 2023
Year: 2023

Building comprehensive brain connectomes has proved of fundamental importance in resting-state fMRI (rs-fMRI) analysis. Based on the foundation of brain network, spatial-temporal-based graph convolutional networks have dramatically improved the performance of deep learning methods in rs-fMRI time series classification. However, existing works either pre-define the brain network as the correlation matrix derived from the raw time series or jointly learn the connectome and model parameters without any topology constraint. These methods could suffer from degraded classification performance caused by the deviation from the intrinsic brain connectivity and lack biological interpretability of demonstrating the causal structure (i.e., effective connectivity) among brain regions. Moreover, most existing methods for effective connectivity learning are unaware of the downstream classification task and cannot sufficiently exploit useful rs-fMRI label information. To address these issues in an end-to-end manner, we model the brain network as a directed acyclic graph (DAG) to discover direct causal connections between brain regions and propose Spatial-Temporal DAG Convolutional Network (ST-DAGCN) to jointly infer effective connectivity and classify rs-fMRI time series by learning brain representations based on nonlinear structural equation model. The optimization problem is formulated into a continuous program and solved with score-based learning method via gradient descent. We evaluate ST-DAGCN on two public rs-fMRI databases. Experiments show that ST-DAGCN outperforms existing models by evident margins in rs-fMRI classification and simultaneously learns meaningful edges of effective connectivity that help understand brain activity patterns and pathological mechanisms in brain disease.
MMBaT: A Multi-task Framework for mmWave-based Human Body Reconstruction and Translation Prediction
Venue: arXiv cs.CV 2023
Year: 2023

Human body reconstruction with Millimeter Wave (mmWave) radar point clouds has gained significant interest due to its ability to work in adverse environments and its capacity to mitigate privacy concerns associated with traditional camera-based solutions. Despite pioneering efforts in this field, two challenges persist. Firstly, raw point clouds contain massive noise points, usually caused by the ambient objects and multi-path effects of Radio Frequency (RF) signals. Recent approaches typically rely on prior knowledge or elaborate preprocessing methods, limiting their applicability. Secondly, even after noise removal, the sparse and inconsistent body-related points pose an obstacle to accurate human body reconstruction. To address these challenges, we introduce mmBaT, a novel multi-task deep learning framework that concurrently estimates the human body and predicts body translations in subsequent frames to extract body-related point clouds. Our method is evaluated on two public datasets that are collected with different radar devices and noise levels. A comprehensive comparison against other state-of-the-art methods demonstrates our method has a superior reconstruction performance and generalization ability from noisy raw data, even when compared to methods provided with body-related point clouds.
SPT: Fine-Tuning Transformer-based Language Models Efficiently with Sparsification
Venue: arXiv cs.DC 2023
Year: 2023

Transformer-based large language models (e.g., BERT and GPT) achieve great success, and fine-tuning, which tunes a pre-trained model on a task-specific dataset, is the standard practice to utilize these models for downstream tasks. However, Transformer fine-tuning has long running time and high memory consumption due to the large size of the models. We propose the SPT system to fine-tune Transformer-based models efficiently by introducing sparsity. We observe that the memory consumption of Transformer mainly comes from storing attention weights for multi-head attention (MHA), and the majority of running time is spent on feed-forward network (FFN). Thus, we design the sparse MHA module, which computes and stores only large attention weights to reduce memory consumption, and the routed FFN module, which dynamically activates a subset of model parameters for each token to reduce computation cost. We implement SPT on PyTorch and customize CUDA kernels to run sparse MHA and routed FFN efficiently. Specifically, we use product quantization to identify the large attention weights and compute attention via sparse matrix multiplication for sparse MHA. For routed FFN, we batch the tokens according to their activated model parameters for efficient computation. We conduct extensive experiments to evaluate SPT on various model configurations. The results show that SPT consistently outperforms well-optimized baselines, reducing the peak memory consumption by up to 50% and accelerating fine-tuning by up to 2.2x.
Collect and Connect Data Leaves to Feature Concepts: Interactive Graph Generation Toward Well-being
Venue: arXiv cs.LG 2023
Year: 2023

Feature concepts and data leaves have been invented using datasets to foster creative thoughts for creating well-being in daily life. The idea, simply put, is to attach selected and collected data leaves that are summaries of event flows to be discovered from corresponding datasets, on the target feature concept representing the well-being aimed. A graph of existing or expected datasets to be attached to a feature concept is generated semi-automatically. Rather than sheer automated generative AI, our work addresses the process of generative artificial and natural intelligence to create the basis for data use and reuse.
Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data
Venue: arXiv cs.LG 2023
Year: 2023

Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.
Rate Outage and Meta-Distribution for Uplink Networks in Finite Block-Length Regime
Venue: arXiv cs.IT 2023
Year: 2023

With the expected proliferation of delay constrained applications, future communication technologies are pushed towards using short codes. The performance using short codes cannot be inferred through classical channel capacity analysis, which intrinsically assumes long codes and vanishing frame error rate (FER). This paper studies the performance of an uplink large-scale network in the finite blocklength regime. Bounds on the spatially averaged rate outage probability as well as the coding rate meta distribution are derived. The results reveal the exact achievable rate for a given blocklength and FER, and demonstrate the discrepancy between the actual network rate and idealistic classical channel capacity.
WRTester: Differential Testing of WebAssembly Runtimes via Semantic-aware Binary Generation
Venue: arXiv cs.SE 2023
Year: 2023

Wasm runtime is a fundamental component in the Wasm ecosystem, as it directly impacts whether Wasm applications can be executed as expected. Bugs in Wasm runtime bugs are frequently reported, thus our research community has made a few attempts to design automated testing frameworks for detecting bugs in Wasm runtimes. However, existing testing frameworks are limited by the quality of test cases, i.e., they face challenges of generating both semantic-rich and syntactic-correct Wasm binaries, thus complicated bugs cannot be triggered. In this work, we present WRTester, a novel differential testing framework that can generated complicated Wasm test cases by disassembling and assembling of real-world Wasm binaries, which can trigger hidden inconsistencies among Wasm runtimes. For further pinpointing the root causes of unexpected behaviors, we design a runtime-agnostic root cause location method to accurately locate bugs. Extensive evaluation suggests that WRTester outperforms SOTA techniques in terms of both efficiency and effectiveness. We have uncovered 33 unique bugs in popular Wasm runtimes, among which 25 have been confirmed.