OpenRaiser/Intern-Atlas huggingface dataset:
How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations
Venue: arXiv cs.LG 2023
Year: 2023

While large language models based on the transformer architecture have demonstrated remarkable in-context learning (ICL) capabilities, understandings of such capabilities are still in an early stage, where existing theory and mechanistic understanding focus mostly on simple scenarios such as learning simple function classes. This paper takes initial steps on understanding ICL in more complex scenarios, by studying learning with representations. Concretely, we construct synthetic in-context learning problems with a compositional structure, where the label depends on the input through a possibly complex but fixed representation function, composed with a linear function that differs in each instance. By construction, the optimal ICL algorithm first transforms the inputs by the representation function, and then performs linear ICL on top of the transformed dataset. We show theoretically the existence of transformers that approximately implement such algorithms with mild depth and size. Empirically, we find trained transformers consistently achieve near-optimal ICL performance in this setting, and exhibit the desired dissection where lower layers transforms the dataset and upper layers perform linear ICL. Through extensive probing and a new pasting experiment, we further reveal several mechanisms within the trained transformers, such as concrete copying behaviors on both the inputs and the representations, linear ICL capability of the upper layers alone, and a post-ICL representation selection mechanism in a harder mixture setting. These observed mechanisms align well with our theory and may shed light on how transformers perform ICL in more realistic scenarios.
Video Language Planning
Venue: arXiv cs.CV 2023
Year: 2023

We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pretrained on Internet-scale data. To this end, we present video language planning (VLP), an algorithm that consists of a tree search procedure, where we train (i) vision-language models to serve as both policies and value functions, and (ii) text-to-video models as dynamics models. VLP takes as input a long-horizon task instruction and current image observation, and outputs a long video plan that provides detailed multimodal (video and language) specifications that describe how to complete the final task. VLP scales with increasing computation budget where more computation time results in improved video plans, and is able to synthesize long-horizon video plans across different robotics domains: from multi-object rearrangement, to multi-camera bi-arm dexterous manipulation. Generated video plans can be translated into real robot actions via goal-conditioned policies, conditioned on each intermediate frame of the generated video. Experiments show that VLP substantially improves long-horizon task success rates compared to prior methods on both simulated and real robots (across 3 hardware platforms).
Data Contamination Through the Lens of Time
Venue: arXiv cs.CL 2023
Year: 2023

Recent claims about the impressive abilities of large language models (LLMs) are often supported by evaluating publicly available benchmarks. Since LLMs train on wide swaths of the internet, this practice raises concerns of data contamination, i.e., evaluating on examples that are explicitly or implicitly included in the training data. Data contamination remains notoriously challenging to measure and mitigate, even with partial attempts like controlled experimentation of training data, canary strings, or embedding similarities. In this work, we conduct the first thorough longitudinal analysis of data contamination in LLMs by using the natural experiment of training cutoffs in GPT models to look at benchmarks released over time. Specifically, we consider two code/mathematical problem-solving datasets, Codeforces and Project Euler, and find statistically significant trends among LLM pass rate vs. GitHub popularity and release date that provide strong evidence of contamination. By open-sourcing our dataset, raw results, and evaluation framework, our work paves the way for rigorous analyses of data contamination in modern models. We conclude with a discussion of best practices and future steps for publicly releasing benchmarks in the age of LLMs that train on webscale data.
Managing Networked IoT Assets Using Practical and Scalable Traffic Inference
Venue: arXiv cs.CR 2023
Year: 2023

The Internet has recently witnessed unprecedented growth of a class of connected assets called the Internet of Things (IoT). Due to relatively immature manufacturing processes and limited computing resources, IoTs have inadequate device-level security measures, exposing the Internet to various cyber risks. Prior research leveraged predictable patterns in IoT network traffic to develop inference models. However, they fall short of expectations in addressing practical challenges, preventing them from being deployed in production settings. This thesis identifies four practical challenges and develops techniques to address them which can help secure businesses and protect user privacy against growing cyber threats. My first contribution balances prediction gains against computing costs of traffic features for IoT traffic classification and monitoring. My second contribution addresses the challenges of measurement costs and data quality. I develop an inference method that uses stochastic and deterministic modeling to predict IoT devices in home networks from opaque and coarse-grained IPFIX flow data. Evaluations show that false positive rates can be reduced by 75% compared to related work without significantly affecting true positives. My third contribution focuses on the challenge of concept drifts by analyzing over six million flow records collected from 12 real home networks. Finally, my fourth contribution studies the resilience of machine learning models against adversarial attacks with a specific focus on decision tree-based models.
Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System
Venue: arXiv cs.CR 2023
Year: 2023

In recent years, the security issues of artificial intelligence have become increasingly prominent due to the rapid development of deep learning research and applications. Backdoor attack is an attack targeting the vulnerability of deep learning models, where hidden backdoors are activated by triggers embedded by the attacker, thereby outputting malicious predictions that may not align with the intended output for a given input. In this work, we propose a novel black-box backdoor attack based on machine unlearning. The attacker first augments the training set with carefully designed samples, including poison and mitigation data, to train a `benign' model. Then, the attacker posts unlearning requests for the mitigation samples to remove the impact of relevant data on the model, gradually activating the hidden backdoor. Since backdoors are implanted during the iterative unlearning process, it significantly increases the computational overhead of existing defense methods for backdoor detection or mitigation. To address this new security threat, we proposes two methods for detecting or mitigating such malicious unlearning requests. We conduct the experiment in both exact unlearning and approximate unlearning (i.e., SISA) settings. Experimental results indicate that: 1) our attack approach can successfully implant backdoor into the model, and sharding increases the difficult of attack; 2) our detection algorithms are effective in identifying the mitigation samples, while sharding reduces the effectiveness of our detection algorithms.
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection
Venue: arXiv cs.CR 2023
Year: 2023

The effectiveness of network intrusion detection systems, predominantly based on machine learning, are highly influenced by the dataset they are trained on. Ensuring an accurate reflection of the multifaceted nature of benign and malicious traffic in these datasets is essential for creating models capable of recognizing and responding to a wide array of intrusion patterns. However, existing datasets often fall short, lacking the necessary diversity and alignment with the contemporary network environment, thereby limiting the effectiveness of intrusion detection. This paper introduces TII-SSRC-23, a novel and comprehensive dataset designed to overcome these challenges. Comprising a diverse range of traffic types and subtypes, our dataset is a robust and versatile tool for the research community. Additionally, we conduct a feature importance analysis, providing vital insights into critical features for intrusion detection tasks. Through extensive experimentation, we also establish firm baselines for supervised and unsupervised intrusion detection methodologies using our dataset, further contributing to the advancement and adaptability of intrusion detection models in the rapidly changing landscape of network security. Our dataset is available at https://kaggle.com/datasets/daniaherzalla/tii-ssrc-23.
Data privacy for Mobility as a Service
Venue: arXiv cs.CR 2023
Year: 2023

Mobility as a Service (MaaS) is revolutionizing the transportation industry by offering convenient, efficient and integrated transportation solutions. However, the extensive use of user data as well as the integration of multiple service providers raises significant privacy concerns. The objective of this survey paper is to provide a comprehensive analysis of the current state of data privacy in MaaS, in particular by discussing the associated challenges, existing solutions as well as potential future directions to ensure user privacy while maintaining the benefits of MaaS systems for society.
Gotta be SAFE: A New Framework for Molecular Design
Venue: arXiv cs.LG 2023
Year: 2023

Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures. To address this issue, we introduce Sequential Attachment-based Fragment Embedding (SAFE), a novel line notation for chemical structures. SAFE reimagines SMILES strings as an unordered sequence of interconnected fragment blocks while maintaining compatibility with existing SMILES parsers. It streamlines complex generative tasks, including scaffold decoration, fragment linking, polymer generation, and scaffold hopping, while facilitating autoregressive generation for fragment-constrained design, thereby eliminating the need for intricate decoding or graph-based models. We demonstrate the effectiveness of SAFE by training an 87-million-parameter GPT2-like model on a dataset containing 1.1 billion SAFE representations. Through targeted experimentation, we show that our SAFE-GPT model exhibits versatile and robust optimization performance. SAFE opens up new avenues for the rapid exploration of chemical space under various constraints, promising breakthroughs in AI-driven molecular design.
Neural Tangent Kernels Motivate Graph Neural Networks with Cross-Covariance Graphs
Venue: arXiv cs.LG 2023
Year: 2023

Neural tangent kernels (NTKs) provide a theoretical regime to analyze the learning and generalization behavior of over-parametrized neural networks. For a supervised learning task, the association between the eigenvectors of the NTK kernel and given data (a concept referred to as alignment in this paper) can govern the rate of convergence of gradient descent, as well as generalization to unseen data. Building upon this concept, we investigate NTKs and alignment in the context of graph neural networks (GNNs), where our analysis reveals that optimizing alignment translates to optimizing the graph representation or the graph shift operator in a GNN. Our results further establish the theoretical guarantees on the optimality of the alignment for a two-layer GNN and these guarantees are characterized by the graph shift operator being a function of the cross-covariance between the input and the output data. The theoretical insights drawn from the analysis of NTKs are validated by our experiments focused on a multi-variate time series prediction task for a publicly available dataset. Specifically, they demonstrate that GNNs with cross-covariance as the graph shift operator indeed outperform those that operate on the covariance matrix from only the input data.
Get-A-Sense: Designing Spatial Context Awareness for Mobile AR Environment Understanding
Venue: arXiv cs.DC 2023
Year: 2023

Physical environment understanding is vital in delivering immersive and interactive mobile augmented reality (AR) user experiences. Recently, we have witnessed a transition in the design of environment understanding systems, from visual data focused to centering on the concept of spatial context, including user, device, and environment information. Even though spatial context can benefit the environment understanding tasks, e.g., we demonstrate in a case study that lighting estimation performance can be improved by as much as 59%, not all environment understanding systems support spatial context. Furthermore, even for the environment understanding systems that support spatial context, not all useful spatial context has been leveraged; and the design and implementation can differ vastly. In this paper, we advocate for the design of a spatial context-aware and shared environment understanding system that can effectively support multiple tasks simultaneously. Fundamentally, there are many practical challenges in designing such a unified environment understanding system. We discuss the challenges in the context of three open questions, (i) how to fully leverage user mobility, (ii) how to design a unified data model, and (iii) finally how to build a shared system for multiple tasks.
Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout
Venue: arXiv cs.LG 2023
Year: 2023

Connected and autonomous vehicles have the potential to minimize energy consumption by optimizing the vehicle velocity and powertrain dynamics with Vehicle-to-Everything info en route. Existing deterministic and stochastic methods created to solve the eco-driving problem generally suffer from high computational and memory requirements, which makes online implementation challenging. This work proposes a hierarchical multi-horizon optimization framework implemented via a neural network. The neural network learns a full-route value function to account for the variability in route information and is then used to approximate the terminal cost in a receding horizon optimization. Simulations over real-world routes demonstrate that the proposed approach achieves comparable performance to a stochastic optimization solution obtained via reinforcement learning, while requiring no sophisticated training paradigm and negligible on-board memory.
NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear Domain
Venue: arXiv cs.CL 2023
Year: 2023

As LLMs have become increasingly popular, they have been used in almost every field. But as the application for LLMs expands from generic fields to narrow, focused science domains, there exists an ever-increasing gap in ways to evaluate their efficacy in those fields. For the benchmarks that do exist, a lot of them focus on questions that don't require proper understanding of the subject in question. In this paper, we present NuclearQA, a human-made benchmark of 100 questions to evaluate language models in the nuclear domain, consisting of a varying collection of questions that have been specifically designed by experts to test the abilities of language models. We detail our approach and show how the mix of several types of questions makes our benchmark uniquely capable of evaluating models in the nuclear domain. We also present our own evaluation metric for assessing LLM's performances due to the limitations of existing ones. Our experiments on state-of-the-art models suggest that even the best LLMs perform less than satisfactorily on our benchmark, demonstrating the scientific knowledge gap of existing LLMs.
TEQ: Trainable Equivalent Transformation for Quantization of LLMs
Venue: arXiv cs.CL 2023
Year: 2023

As large language models (LLMs) become more prevalent, there is a growing need for new and improved quantization methods that can meet the computationalast layer demands of these modern architectures while maintaining the accuracy. In this paper, we present TEQ, a trainable equivalent transformation that preserves the FP32 precision of the model output while taking advantage of low-precision quantization, especially 3 and 4 bits weight-only quantization. The training process is lightweight, requiring only 1K steps and fewer than 0.1 percent of the original model's trainable parameters. Furthermore, the transformation does not add any computational overhead during inference. Our results are on-par with the state-of-the-art (SOTA) methods on typical LLMs. Our approach can be combined with other methods to achieve even better performance. The code is available at https://github.com/intel/neural-compressor.
A Local Graph Limits Perspective on Sampling-Based GNNs
Venue: arXiv cs.LG 2023
Year: 2023

We propose a theoretical framework for training Graph Neural Networks (GNNs) on large input graphs via training on small, fixed-size sampled subgraphs. This framework is applicable to a wide range of models, including popular sampling-based GNNs, such as GraphSAGE and FastGCN. Leveraging the theory of graph local limits, we prove that, under mild assumptions, parameters learned from training sampling-based GNNs on small samples of a large input graph are within an $ε$-neighborhood of the outcome of training the same architecture on the whole graph. We derive bounds on the number of samples, the size of the graph, and the training steps required as a function of $ε$. Our results give a novel theoretical understanding for using sampling in training GNNs. They also suggest that by training GNNs on small samples of the input graph, practitioners can identify and select the best models, hyperparameters, and sampling algorithms more efficiently. We empirically illustrate our results on a node classification task on large citation graphs, observing that sampling-based GNNs trained on local subgraphs 12$\times$ smaller than the original graph achieve comparable performance to those trained on the input graph.
Design of a fault free Inverter Circuit using Minimum number of cells & related Kink Energy Calculation in Quantum dot Cellular Automata
Venue: arXiv cs.ET 2023
Year: 2023

Quantum dot Cellular Automata (QCA) is the most promising nanotechnology in the field of microelectronics and VLSI systems. QCA-based circuits require less power with a high switching speed of operation compared to CMOS technology. QCA inverter is one of the basic building blocks of QCA circuit design. The conventional QCA inverter design requires many cells. In this paper, we design the QCA inverter circuit using a lesser number of cells. We showed the kink energy calculation for the QCA-implemented inverters as well as the polarization of the circuits.
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning
Venue: arXiv cs.CV 2023
Year: 2023

This paper presents a method based on a kernel dictionary learning algorithm for segmenting brain tumor regions in magnetic resonance images (MRI). A set of first-order and second-order statistical feature vectors are extracted from patches of size 3 * 3 around pixels in the brain MRI scans. These feature vectors are utilized to train two kernel dictionaries separately for healthy and tumorous tissues. To enhance the efficiency of the dictionaries and reduce training time, a correlation-based sample selection technique is developed to identify the most informative and discriminative subset of feature vectors. This technique aims to improve the performance of the dictionaries by selecting a subset of feature vectors that provide valuable information for the segmentation task. Subsequently, a linear classifier is utilized to distinguish between healthy and unhealthy pixels based on the learned dictionaries. The results demonstrate that the proposed method outperforms other existing methods in terms of segmentation accuracy and significantly reduces both the time and memory required, resulting in a remarkably fast training process.
NICE: Improving Panoptic Narrative Detection and Segmentation with Cascading Collaborative Learning
Venue: arXiv cs.CV 2023
Year: 2023

Panoptic Narrative Detection (PND) and Segmentation (PNS) are two challenging tasks that involve identifying and locating multiple targets in an image according to a long narrative description. In this paper, we propose a unified and effective framework called NICE that can jointly learn these two panoptic narrative recognition tasks. Existing visual grounding tasks use a two-branch paradigm, but applying this directly to PND and PNS can result in prediction conflict due to their intrinsic many-to-many alignment property. To address this, we introduce two cascading modules based on the barycenter of the mask, which are Coordinate Guided Aggregation (CGA) and Barycenter Driven Localization (BDL), responsible for segmentation and detection, respectively. By linking PNS and PND in series with the barycenter of segmentation as the anchor, our approach naturally aligns the two tasks and allows them to complement each other for improved performance. Specifically, CGA provides the barycenter as a reference for detection, reducing BDL's reliance on a large number of candidate boxes. BDL leverages its excellent properties to distinguish different instances, which improves the performance of CGA for segmentation. Extensive experiments demonstrate that NICE surpasses all existing methods by a large margin, achieving 4.1% for PND and 2.9% for PNS over the state-of-the-art. These results validate the effectiveness of our proposed collaborative learning strategy. The project of this work is made publicly available at https://github.com/Mr-Neko/NICE.
A High Fidelity and Low Complexity Neural Audio Coding
Venue: arXiv cs.SD 2023
Year: 2023

Audio coding is an essential module in the real-time communication system. Neural audio codecs can compress audio samples with a low bitrate due to the strong modeling and generative capabilities of deep neural networks. To address the poor high-frequency expression and high computational cost and storage consumption, we proposed an integrated framework that utilizes a neural network to model wide-band components and adopts traditional signal processing to compress high-band components according to psychological hearing knowledge. Inspired by auditory perception theory, a perception-based loss function is designed to improve harmonic modeling. Besides, generative adversarial network (GAN) compression is proposed for the first time for neural audio codecs. Our method is superior to prior advanced neural codecs across subjective and objective metrics and allows real-time inference on desktop and mobile.