In recent years, microservices have gained widespread adoption in IT operations due to their scalability, maintenance, and flexibility. However, it becomes challenging for site reliability engineers (SREs) to pinpoint the root cause due to the complex relationship in microservices when facing system malfunctions. Previous research employed structure learning methods (e.g., PC-algorithm) to establish causal relationships and derive root causes from causal graphs. Nevertheless, they ignored the temporal order of time series data and failed to leverage the rich information inherent in the temporal relationships. For instance, in cases where there is a sudden spike in CPU utilization, it can lead to an increase in latency for other microservices. However, in this scenario, the anomaly in CPU utilization occurs before the latency increases, rather than simultaneously. As a result, the PC-algorithm fails to capture such characteristics. To address these challenges, we propose RUN, a novel approach for root cause analysis using neural Granger causal discovery with contrastive learning. RUN enhances the backbone encoder by integrating contextual information from time series and leverages a time series forecasting model to conduct neural Granger causal discovery. In addition, RUN incorporates Pagerank with a personalization vector to efficiently recommend the top-k root causes. Extensive experiments conducted on the synthetic and real-world microservice-based datasets demonstrate that RUN noticeably outperforms the state-of-the-art root cause analysis methods. Moreover, we provide an analysis scenario for the sock-shop case to showcase the practicality and efficacy of RUN in microservice-based applications. Our code is publicly available at https://github.com/zmlin1998/RUN.
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Anchor-based large-scale multi-view clustering has attracted considerable attention for its effectiveness in handling massive datasets. However current methods mainly seek the consensus embedding feature for clustering by exploring global correlations between anchor graphs or projection matrices.In this paper we propose a simple yet efficient scalable multi-view tensor clustering (S2MVTC) approach where our focus is on learning correlations of embedding features within and across views. Specifically we first construct the embedding feature tensor by stacking the embedding features of different views into a tensor and rotating it. Additionally we build a novel tensor low-frequency approximation (TLFA) operator which incorporates graph similarity into embedding feature learning efficiently achieving smooth representation of embedding features within different views. Furthermore consensus constraints are applied to embedding features to ensure inter-view semantic consistency. Experimental results on six large-scale multi-view datasets demonstrate that S2MVTC significantly outperforms state-of-the-art algorithms in terms of clustering performance and CPU execution time especially when handling massive data. The code of S2MVTC is publicly available at https://github.com/longzhen520/S2MVTC.
SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
PDF ↗We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian Splatting. Gaussian Splatting has recently become very popular as it yields realistic rendering while being significantly faster to train than NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D Gaussians as these Gaussians tend to be unorganized after optimization and no method has been proposed so far. Our first key contribution is a regularization term that encourages the Gaussians to align well with the surface of the scene. We then introduce a method that exploits this alignment to extract a mesh from the Gaussians using Poisson reconstruction which is fast scalable and preserves details in contrast to the Marching Cubes algorithm usually applied to extract meshes from Neural SDFs. Finally we introduce an optional refinement strategy that binds Gaussians to the surface of the mesh and jointly optimizes these Gaussians and the mesh through Gaussian splatting rendering. This enables easy editing sculpting animating and relighting of the Gaussians by manipulating the mesh instead of the Gaussians themselves. Retrieving such an editable mesh for realistic rendering is done within minutes with our method compared to hours with the state-of-the-art method on SDFs while providing a better rendering quality.
Underwater visual understanding has recently gained increasing attention within the computer vision community for studying and monitoring underwater ecosystems. Among these coral reefs play an important and intricate role often referred to as the rainforests of the sea due to their rich biodiversity and crucial environmental impact. Existing coral analysis due to its technical complexity requires significant manual work from coral biologists therefore hindering scalable and comprehensive studies. In this paper we introduce CoralSCOP the first foundation model designed for the automatic dense segmentation of coral reefs. CoralSCOP is developed to accurately assign labels to different coral entities addressing the challenges in the semantic analysis of coral imagery. Its main objective is to identify and delineate the irregular boundaries between various coral individuals across different granularities such as coral/non-coral growth form and genus. This task is challenging due to the semantic agnostic nature or fixed limited semantic categories of previous generic segmentation methods which fail to adequately capture the complex characteristics of coral structures. By introducing a novel parallel semantic branch CoralSCOP can produce high-quality coral masks with semantics that enable a wide range of downstream coral reef analysis tasks. We demonstrate that CoralSCOP exhibits a strong zero-shot ability to segment unseen coral images. To effectively train our foundation model we propose CoralMask a new dataset with 41297 densely labeled coral images and 330144 coral masks. We have conducted comprehensive and extensive experiments to demonstrate the advantages of CoralSCOP over existing generalist segmentation algorithms and coral reef analytical approaches.
To achieve greater accuracy hypergraph matching algorithms require exponential increases in computational resources. Recent kd-tree-based approximate nearest neighbor (ANN) methods despite the sparsity of their compatibility tensor still require exhaustive calculations for large-scale graph matching. This work utilizes CUR tensor decomposition and introduces a novel cascaded second and third-order hypergraph matching framework (CURSOR) for efficient hypergraph matching. A CUR-based second-order graph matching algorithm is used to provide a rough match and then the core of CURSOR a fiber-CUR-based tensor generation method directly calculates entries of the compatibility tensor by leveraging the initial second-order match result. This significantly decreases the time complexity and tensor density. A probability relaxation labeling (PRL)-based matching algorithm especially suitable for sparse tensors is developed. Experiment results on large-scale synthetic datasets and widely-adopted benchmark sets demonstrate the superiority of CURSOR over existing methods. The tensor generation method in CURSOR can be integrated seamlessly into existing hypergraph matching methods to improve their performance and lower their computational costs.
6DoF Pose estimation has been gaining increased importance in vision for over a decade however it does not yet meet the reliability and accuracy standards for mass deployment in industrial robotics. To this effect we present the Industrial Plenoptic Dataset (IPD): the first dataset for the co-evaluation of cameras HDR and algorithms targeted at reliable high-accuracy industrial automation. Specifically we capture 2300 physical scenes of 20 industrial parts covering a 1mx1mx0.5m working volume resulting in over 100000 distinct object views. Each scene is captured with 13 well-calibrated multi-modal cameras including polarization and high-resolution structured light. In terms of lighting we capture each scene at 4 exposures and in 3 challenging lighting conditions ranging from 100 lux to 100000 lux. We also present validate and analyze robot consistency an evaluation method targeted at scalable high accuracy evaluation. We hope that vision systems that succeed on this dataset will have direct industry impact. The dataset and evaluation code are available at https://github.com/intrinsic-ai/ipd.
Interactive motion synthesis is essential in creating immersive experiences in entertainment applications such as video games and virtual reality. However generating animations that are both high-quality and contextually responsive remains a challenge. Traditional techniques in the game industry can produce high-fidelity animations but suffer from high computational costs and poor scalability. Trained neural network models alleviate the memory and speed issues yet fall short on generating diverse motions. Diffusion models offer diverse motion synthesis with low memory usage but require expensive reverse diffusion processes. This paper introduces the Accelerated Auto-regressive Motion Diffusion Model (AAMDM) a novel motion synthesis framework designed to achieve quality diversity and efficiency all together. AAMDM integrates Denoising Diffusion GANs as a fast Generation Module and an Auto-regressive Diffusion Model as a Polishing Module. Furthermore AAMDM operates in a lower-dimensional embedded space rather than the full-dimensional pose space which reduces the training complexity as well as further improves the performance. We show that AAMDM outperforms existing methods in motion quality diversity and runtime efficiency through comprehensive quantitative analyses and visual comparisons. We also demonstrate the effectiveness of each algorithmic component through ablation studies.
Deep functional maps have emerged in recent years as a prominent learning-based framework for non-rigid shape matching problems. While early methods in this domain only focused on learning in the functional domain the latest techniques have demonstrated that by promoting consistency between functional and pointwise maps leads to significant improvements in accuracy. Unfortunately existing approaches rely heavily on the computation of large dense matrices arising from soft pointwise maps which compromises their efficiency and scalability. To address this limitation we introduce a novel memory-scalable and efficient functional map learning pipeline. By leveraging the specific structure of functional maps we offer the possibility to achieve identical results without ever storing the pointwise map in memory. Furthermore based on the same approach we present a differentiable map refinement layer adapted from an existing axiomatic refinement algorithm. Unlike many functional map learning methods which use this algorithm at a post-processing step ours can be easily used at train time enabling to enforce consistency between the refined and initial versions of the map. Our resulting approach is both simpler more efficient and more numerically stable by avoiding differentiation through a linear system while achieving close to state-of-the-art results in challenging scenarios.
Diffusion models (DMs) have exhibited superior performance in generating high-quality and diverse images. However this exceptional performance comes at the cost of expensive generation process particularly due to the heavily used attention module in leading models. Existing works mainly adopt a retraining process to enhance DM efficiency. This is computationally expensive and not very scalable. To this end we introduce the Attention-driven Training-free Efficient Diffusion Model (AT-EDM) framework that leverages attention maps to perform run-time pruning of redundant tokens without the need for any retraining. Specifically for single-denoising-step pruning we develop a novel ranking algorithm Generalized Weighted Page Rank (G-WPR) to identify redundant tokens and a similarity-based recovery method to restore tokens for the convolution operation. In addition we propose a Denoising-Steps-Aware Pruning (DSAP) approach to adjust the pruning budget across different denoising timesteps for better generation quality. Extensive evaluations show that AT-EDM performs favorably against prior art in terms of efficiency (e.g. 38.8% FLOPs saving and up to 1.53x speed-up over Stable Diffusion XL) while maintaining nearly the same FID and CLIP scores as the full model.
CROWD: Certified Robustness via Weight Distribution for Smoothed Classifiers against Backdoor Attack
PDF ↗Language models are vulnerable to clandestinely modified data and manipulation by attackers. Despite considerable research dedicated to enhancing robustness against adversarial attacks, the realm of provable robustness for backdoor attacks remains relatively unexplored. In this paper, we initiate a pioneering investigation into the certified robustness of NLP models against backdoor triggers.We propose a model-agnostic mechanism for large-scale models that applies to complex model structures without the need for assessing model architecture or internal knowledge. More importantly, we take recent advances in randomized smoothing theory and propose a novel weight-based distribution algorithm to enable semantic similarity and provide theoretical robustness guarantees.Experimentally, we demonstrate the efficacy of our approach across a diverse range of datasets and tasks, highlighting its utility in mitigating backdoor triggers. Our results show strong performance in terms of certified accuracy, scalability, and semantic preservation.
To enhance the performance of large language models (LLM) on downstream tasks, one solution is to fine-tune certain LLM parameters and make them better align with the characteristics of the training dataset. This process is commonly known as parameter-efficient fine-tuning (PEFT). Due to the scale of LLM, PEFT operations are usually executed in the public environment (e.g., cloud server). This necessitates sharing sensitive user data across public environments, thereby raising potential privacy concerns. To tackle these challenges, we propose a distributed PEFT framework called DLoRA. DLoRA enables scalable PEFT operations to be performed collaboratively between the cloud and user devices. Coupled with the proposed Kill and Revive algorithm, the evaluation results demonstrate that DLoRA can significantly reduce the computation and communication workload over user devices while achieving superior accuracy and privacy protection.
Instruct, Not Assist: LLM-based Multi-Turn Planning and Hierarchical Questioning for Socratic Code Debugging
PDF ↗Socratic questioning is an effective teaching strategy, encouraging critical thinking and problem-solving. The conversational capabilities of large language models (LLMs) show great potential for providing scalable, real-time student guidance. However, current LLMs often give away solutions directly, making them ineffective instructors. We tackle this issue in the code debugging domain with TreeInstruct, an Instructor agent guided by a novel state space-based planning algorithm. TreeInstruct asks probing questions to help students independently identify and resolve errors. It estimates a student’s conceptual and syntactical knowledge to dynamically construct a question tree based on their responses and current knowledge state, effectively addressing both independent and dependent mistakes concurrently in a multi-turn interaction setting. In addition to using an existing single-bug debugging benchmark, we construct a more challenging multi-bug dataset of 150 coding problems, incorrect solutions, and bug fixes– all carefully constructed and annotated by experts. Extensive evaluation shows TreeInstruct’s state-of-the-art performance on both datasets, proving it to be a more effective instructor than baselines. Furthermore, a real-world case study with five students of varying skill levels further demonstrates TreeInstruct’s ability to guide students to debug their code efficiently with minimal turns and highly Socratic questioning.
Combining Large Language Models (LLMs) with heuristic search algorithms like A* holds the promise of enhanced LLM reasoning and scalable inference. To accelerate training and reduce computational demands, we investigate the coreset selection problem for the training data of LLM heuristic learning. Few methods to learn the heuristic functions consider the interaction between the search algorithm and the machine learning model. In this work, we empirically disentangle the requirements of A* search algorithm from the requirements of the LLM to generalise on this task. Surprisingly, we find an overlap between their requirements; A* requires more accurate predictions on search nodes near the goal, and LLMs need the same set of nodes for effective generalisation. With these insights, we derive a data-selection distribution for learning LM-based heuristics. On three classical planning domains, maze navigation, Sokoban and sliding tile puzzles, our technique reduces the number of iterations required to find the solutions by up to 15x, with a wall-clock speed-up of search up to 5x. The code has been made available at https://github.com/devaansh100/a_star.
We study the problem of fine-tuning a language model (LM) for a target task by optimally using the information from n auxiliary tasks. This problem has broad applications in NLP, such as targeted instruction tuning and data selection in chain-of-thought fine-tuning. The key challenge of this problem is that not all auxiliary tasks are useful to improve the performance of the target task. Thus, choosing the right subset of auxiliary tasks is crucial. Conventional subset selection methods, such as forward & backward selection, are unsuitable for LM fine-tuning because they require repeated training on subsets of auxiliary tasks. This paper introduces a new algorithm to estimate model fine-tuning performances without repeated training. Our algorithm first performs multitask training using the data of all the tasks to obtain a meta initialization. Then, we approximate the model fine-tuning loss of a subset using functional values and gradients from the meta initialization. Empirically, we find that this gradient-based approximation holds with remarkable accuracy for twelve transformer-based LMs. Thus, we can now estimate fine-tuning performances on CPUs within a few seconds. We conduct extensive experiments to validate our approach, delivering a speedup of 30\times over conventional subset selection while incurring only 1% error of the true fine-tuning performances. In downstream evaluations of instruction tuning and chain-of-thought fine-tuning, our approach improves over prior methods that utilize gradient or representation similarity for subset selection by up to 3.8%.
Extensive efforts have been made before the public release of Large language models (LLMs) to align their behaviors with human values. However, even meticulously aligned LLMs remain vulnerable to malicious manipulations such as jailbreaking, leading to unintended behaviors. In this work, we propose a novel black-box jailbreak framework for automated red teaming of LLMs. We designed malicious content concealing and memory reframing with an iterative optimization algorithm to jailbreak LLMs, motivated by the research about the distractibility and over-confidence phenomenon of LLMs. Extensive experiments of jailbreaking both open-source and proprietary LLMs demonstrate the superiority of our framework in terms of effectiveness, scalability and transferability. We also evaluate the effectiveness of existing jailbreak defense methods against our attack and highlight the crucial need to develop more effective and practical defense strategies.
Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge
PDF ↗Despite progress in multimodal large language models (MLLMs), the challenge of interpreting long-form videos in response to linguistic queries persists, largely due to the inefficiency in temporal grounding and limited pre-trained context window size. In this work, we introduce Temporal Grounding Bridge (TGB), a novel framework that bootstraps MLLMs with advanced temporal grounding capabilities and broadens their contextual scope. Our framework significantly enhances the temporal capabilities of current MLLMs through three key innovations: an efficient multi-span temporal grounding algorithm applied to low-dimension temporal features projected from flow; a multimodal length extrapolation training paradigm that utilizes low-dimension temporal features to extend the training context window size; and a bootstrapping framework that bridges our model with pluggable MLLMs without requiring annotation. We validate TGB across seven video benchmarks and demonstrate substantial performance improvements compared with prior MLLMs. Notably, our model, initially trained on sequences of four frames, effectively handles sequences up to 16 longer without sacrificing performance, highlighting its scalability and effectiveness in real-world applications. Our code is publicly available.
Language Language Models (LLMs) face safety concerns due to potential misuse by malicious users. Recent red-teaming efforts have identified adversarial suffixes capable of jailbreaking LLMs using the gradient-based search algorithm Greedy Coordinate Gradient (GCG). However, GCG struggles with computational inefficiency, limiting further investigations regarding suffix transferability and scalability across models and data. In this work, we bridge the connection between search efficiency and suffix transferability. We propose a two-stage transfer learning framework, DeGCG, which decouples the search process into behavior-agnostic pre-searching and behavior-relevant post-searching. Specifically, we employ direct first target token optimization in pre-searching to facilitate the search process. We apply our approach to cross-model, cross-data, and self-transfer scenarios. Furthermore, we introduce an interleaved variant of our approach, i-DeGCG, which iteratively leverages self-transferability to accelerate the search process. Experiments on HarmBench demonstrate the efficiency of our approach across various models and domains. Notably, our i-DeGCG outperforms the baseline on Llama2-chat-7b with ASRs of 43.9 (+ 22.2) and 39.0 (+19.5) on valid and test sets, respectively. Further analysis on cross-model transfer indicates the pivotal role of first target token optimization in leveraging suffix transferability for efficient searching.
Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. In many domains, adversarial training has proven to be one of the most promising methods to reliably improve robustness against such attacks. Yet, in the context of LLMs, current methods for adversarial training are hindered by the high computational costs required to perform discrete adversarial attacks at each training iteration. We address this problem by instead calculating adversarial attacks in the continuous embedding space of the LLM, which is orders of magnitudes more efficient. We propose a fast adversarial training algorithm (C-AdvUL) composed of two losses: the first makes the model robust on continuous embedding attacks computed on an adversarial behaviour dataset; the second ensures the usefulness of the final model by fine-tuning on utility data. Moreover, we introduce C-AdvIPO, an adversarial variant of IPO that does not require utility data for adversarially robust alignment. Our empirical evaluation on five models from different families (Gemma, Phi3, Mistral, Zephyr, Llama2) and at different scales (2B, 3.8B, 7B) shows that both algorithms substantially enhance LLM robustness against discrete attacks (GCG, AutoDAN, PAIR), while maintaining utility. Our results demonstrate that robustness to continuous perturbations can extrapolate to discrete threat models. Thereby, we present a path toward scalable adversarial training algorithms for robustly aligning LLMs.
Optimization · Discrete and Combinatorial Optimization
Neural network pruning is a key technique towards engineering large yet scalable, interpretable, and generalizable models. Prior work on the subject has developed largely along two orthogonal directions: (1) differentiable pruning for efficiently and accurately scoring the importance of parameters, and (2) combinatorial optimization for efficiently searching over the space of sparse models. We unite the two approaches, both theoretically and empirically, to produce a coherent framework for structured neural network pruning in which differentiable pruning guides combinatorial optimization algorithms to select the most important sparse set of parameters. Theoretically, we show how many existing differentiable pruning techniques can be understood as nonconvex regularization for group sparse optimization, and prove that for a wide class of nonconvex regularizers, the global optimum is unique, group-sparse, and provably yields an approximate solution to a sparse convex optimization problem. The resulting algorithm that we propose, SequentialAttention++, advances the state of the art in large-scale neural network block-wise pruning tasks on the ImageNet and Criteo datasets.
Social Aspects
We consider the problem of recovering the ground truth ordering (ranking, top-$k$, or others) over a large number of alternatives. The wisdom of crowd is a heuristic approach based on Condorcet's Jury theorem to address this problem through collective opinions.This approach fails to recover the ground truth when the majority of the crowd is misinformed. The \emph{surprisingly popular} (SP) algorithm~\citep{prelec2017solution} is an alternative approach that is able to recover the ground truth even when experts are in minority. The SP algorithm requires the voters to predict other voters' report in the form of a full probability distribution over all rankings of alternatives. However, when the number of alternatives, $m$, is large, eliciting the prediction report or even the vote over $m$ alternatives might be too costly. In this paper, we design a scalable alternative of the SP algorithm which only requires eliciting partial preferences from the voters, and propose new variants of the SP algorithm. In particular, we propose two versions---\emph{Aggregated-SP} and \emph{Partial-SP}---that ask voters to report vote and prediction on a subset of size $k$ ($\ll m$) in terms of top alternative, partial rank, or an approval set. Through a large-scale crowdsourcing experiment on MTurk, we show that both of our approaches outperform conventional preference aggregation algorithms for the recovery of ground truth rankings, when measured in terms of Kendall-Tau distance and Spearman's $\rho$. We further analyze the collected data and demonstrate that voters' behavior in the experiment, including the minority of the experts, and the SP phenomenon, can be correctly simulated by a concentric mixtures of Mallows model. Finally, we provide theoretical bounds on the sample complexity of SP algorithms with partial rankings to demonstrate the theoretical guarantees of the proposed methods.