GenAI4ELab/papercli-papers huggingface dataset:
BAN: Detecting Backdoors Activated by Adversarial Neuron Noise
Authors: Xiaoyun Xu; Zhuoran Liu; Stefanos Koffas; Shujian Yu; Stjepan Picek
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Backdoor attacks on deep learning represent a recent threat that has gained significant attention in the research community. 
Backdoor defenses are mainly based on backdoor inversion, which has been shown to be generic, model-agnostic, and applicable to practical threat scenarios. State-of-the-art backdoor inversion recovers a mask in the feature space to locate prominent backdoor features, where benign and backdoor features can be disentangled.  However, it suffers from high computational overhead, and we also find that it overly relies on prominent backdoor features that are highly distinguishable from benign features. To tackle these shortcomings, this paper improves backdoor feature inversion for backdoor detection by incorporating extra neuron activation information. In particular, we adversarially increase the loss of backdoored models with respect to weights to activate the backdoor effect, based on which we can easily differentiate backdoored and clean models. Experimental results demonstrate our defense, BAN, is 1.37$\times$ (on CIFAR-10) and 5.11$\times$ (on ImageNet200) more efficient with an average 9.99\% higher detect success rate than the state-of-the-art defense BTI DBF. Our code and trained models are publicly available at https://github.com/xiaoyunxxy/ban.
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning Rate
Authors: Fan-Ming Luo; Zuolin Tu; Zefang Huang; Yang Yu
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Real-world decision-making tasks are usually partially observable Markov decision processes (POMDPs), where the state is not fully observable. Recent progress has demonstrated that recurrent reinforcement learning (RL), which consists of a context encoder based on recurrent neural networks (RNNs) for unobservable state prediction and a multilayer perceptron (MLP) policy for decision making, can mitigate partial observability and serve as a robust baseline for POMDP tasks. However, prior recurrent RL algorithms have faced issues with training instability. In this paper, we find that this instability stems from the autoregressive nature of RNNs, which causes even small changes in RNN parameters to produce large output variations over long trajectories. Therefore, we propose **R**ecurrent Off-policy RL with Context-**E**ncoder-**S**p**e**cific **L**earning Rate (RESeL) to tackle this issue. Specifically, RESeL uses a lower learning rate for context encoder than other MLP layers to ensure the stability of the former while maintaining the training efficiency of the latter. We integrate this technique into existing off-policy RL methods, resulting in the RESeL algorithm. We evaluated RESeL in 18 POMDP tasks, including classic, meta-RL, and credit assignment scenarios, as well as five MDP locomotion tasks. The experiments demonstrate significant improvements in training stability with RESeL. Comparative results show that RESeL achieves notable performance improvements over previous recurrent RL baselines in POMDP tasks, and is competitive with or even surpasses state-of-the-art methods in MDP tasks. Further ablation studies highlight the necessity of applying a distinct learning rate for the context encoder. Code is available at https://github.com/FanmingL/Recurrent-Offpolicy-RL.
Understanding Emergent Abilities of Language Models from the Loss Perspective
Authors: Zhengxiao Du; Aohan Zeng; Yuxiao Dong; Jie Tang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Recent studies have put into question the belief that emergent abilities in language models are exclusive to large models. This skepticism arises from two observations: 1) smaller models can also exhibit high performance on emergent abilities and 2) there is doubt on the discontinuous metrics used to measure these abilities. In this paper, we propose to study emergent abilities in the lens of pre-training loss, instead of model size or training compute. We demonstrate that the Transformer models with the same pre-training loss, but different model and data sizes, generate the same performance on various downstream tasks, with a fixed data corpus, tokenization, and model architecture. We also discover that a model exhibits emergent abilities on certain tasks—regardless of the continuity of metrics—when its pre-training loss falls below a specific threshold. Before reaching this threshold, its performance remains at the level of random guessing. This inspires us to redefine emergent abilities as those that manifest in models with lower pre-training losses, highlighting that these abilities cannot be predicted by merely extrapolating the performance trends of models with higher pre-training losses.
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking
Authors: Xinyu Zhou; Jinglun Li; Lingyi Hong; Kaixun Jiang; Pinxue Guo; Weifeng Ge; Wenqiang Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. Image-feature regression methods heavily depend on matching results and do not utilize positional prior, while the autoregressive approach can only be trained using bounding boxes available in the training set, potentially resulting in suboptimal performance during testing with unseen data. Inspired by the diffusion model, denoising learning enhances the model’s robustness to unseen data. Therefore, We introduce noise to bounding boxes, generating noisy boxes for training, thus enhancing model robustness on testing data. We propose a new paradigm to formulate the visual object tracking problem as a denoising learning process. However, tracking algorithms are usually asked to run in real-time, directly applying the diffusion model to object tracking would severely impair tracking speed. Therefore, we decompose the denoising learning process into every denoising block within a model, not by running the model multiple times, and thus we summarize the proposed paradigm as an in-model latent denoising learning process. Specifically, we propose a denoising Vision Transformer (ViT), which is composed of multiple denoising blocks. In the denoising block, template and search embeddings are projected into every denoising block as conditions. A denoising block is responsible for removing the noise in a predicted bounding box, and multiple stacked denoising blocks cooperate to accomplish the whole denoising process. Subsequently, we
utilize image features and trajectory information to refine the denoised bounding box. Besides, we also utilize trajectory memory and visual memory to improve tracking stability. Experimental results validate the effectiveness of our approach, achieving competitive performance on several challenging datasets. The proposed in-model latent denoising tracker achieve real-time speed, rendering denoising learning applicable in the visual object tracking community.
Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
Authors: Xiaoyu Kong; Jiancan Wu; An Zhang; Leheng Sheng; Hui Lin; Xiang Wang; Xiangnan He
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Sequential recommendation systems predict the next interaction item based on users' past interactions, aligning recommendations with individual preferences. Leveraging the strengths of Large Language Models (LLMs) in knowledge comprehension and reasoning, recent approaches  are eager to apply LLMs to sequential recommendation. A common  paradigm is converting user behavior sequences into instruction data, and fine-tuning the LLM with parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaption (LoRA). However, the uniform application of LoRA across diverse user behaviors is insufficient to capture individual variability, resulting in negative transfer between disparate sequences.
To address these challenges, we propose Instance-wise LoRA (iLoRA). We innovatively treat the sequential recommendation task as a form of multi-task learning, integrating LoRA with the Mixture of Experts (MoE) framework. This approach encourages different experts to capture various aspects of user behavior. Additionally, we introduce a sequence representation guided gate function that generates customized expert participation weights for each user sequence, which allows dynamic parameter adjustment for instance-wise recommendations. 
In sequential recommendation, iLoRA achieves an average relative improvement of 11.4\% over basic LoRA in the hit ratio metric, with less than a 1\% relative increase in trainable parameters.
Extensive experiments on three benchmark datasets demonstrate the effectiveness of iLoRA, highlighting its superior performance compared to existing methods in mitigating negative transfer and improving recommendation accuracy.
Our data and code are available at https://github.com/AkaliKong/iLoRA.
Discrete Flow Matching
Authors: Itai Gat; Tal Remez; Neta Shaul; Felix Kreuk; Ricky T. Q. Chen; Gabriel Synnaeve; Yossi Adi; Yaron Lipman
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Despite Flow Matching and diffusion models having emerged as powerful generative paradigms for continuous variables such as images and videos, their application to high-dimensional discrete data, such as language, is still limited.  In this work, we present Discrete Flow Matching, a novel discrete flow paradigm designed specifically for generating discrete data.  Discrete Flow Matching offers several key contributions:  (i) it works with a general family of probability paths interpolating between source and target distributions; (ii) it allows for a generic formula for sampling from these probability paths using learned posteriors such as the probability denoiser ($x$-prediction) and noise-prediction ($\epsilon$-prediction); (iii) practically, focusing on specific probability paths defined with different schedulers improves generative perplexity compared to previous discrete diffusion and flow models; and (iv) by scaling Discrete Flow Matching models up to 1.7B parameters, we reach 6.7% Pass@1 and 13.4% Pass@10 on HumanEval and 6.7% Pass@1 and 20.6% Pass@10 on 1-shot MBPP coding benchmarks. Our approach is capable of generating high-quality discrete data in a non-autoregressive fashion, significantly closing the gap between autoregressive models and discrete flow models.
Mitigating Spurious Correlations via Disagreement Probability
Authors: Hyeonggeun Han; Sehwan Kim; Hyungjun Joo; Sangwoo Hong; Jungwoo Lee
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Models trained with empirical risk minimization (ERM) are prone to be biased towards spurious correlations between target labels and bias attributes, which leads to poor performance on data groups lacking spurious correlations. It is particularly challenging to address this problem when access to bias labels is not permitted. To mitigate the effect of spurious correlations without bias labels, we first introduce a novel training objective designed to robustly enhance model performance across all data samples, irrespective of the presence of spurious correlations. From this objective, we then derive a debiasing method, Disagreement Probability based Resampling for debiasing (DPR), which does not require bias labels. DPR leverages the disagreement between the target label and the prediction of a biased model to identify bias-conflicting samples—those without spurious correlations—and upsamples them according to the disagreement probability. Empirical evaluations on multiple benchmarks demonstrate that DPR achieves state-of-the-art performance over existing baselines that do not use bias labels. Furthermore, we provide a theoretical analysis that details how DPR reduces dependency on spurious correlations.
The Power of Hard Attention Transformers on Data Sequences: A formal language theoretic perspective
Authors: Pascal Bergsträßer; Chris Köcher; Anthony Widjaja Lin; Georg Zetzsche
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Formal language theory has recently been successfully employed to unravel 
    the power of transformer encoders. This setting is primarily applicable in
    Natural Language Processing (NLP), as a token embedding function (where
    a bounded number of tokens is admitted) is first applied before feeding 
    the input to the transformer. 
    On certain kinds of data (e.g. time 
    series), we want our transformers to be able to handle arbitrary
    input sequences of numbers (or tuples thereof) without a priori
    limiting the values of these numbers. In this
    paper, we initiate the study of the expressive power of transformer encoders
    on sequences of data (i.e. tuples of numbers).
    Our results indicate an increase in expressive power of 
    hard attention transformers over data sequences, in stark contrast to the 
    case of strings.
    In particular, we prove that Unique Hard Attention Transformers (UHAT) over 
    inputs as data sequences no longer lie within the circuit complexity 
    class AC0 (even without positional encodings), unlike the case of string
    inputs, 
    but are still within the complexity class TC0 (even with positional
    encodings). Over strings, UHAT without positional encodings capture only
    regular languages. In contrast, we show that over data sequences
    UHAT can capture non-regular properties.
    Finally, we show that UHAT capture languages 
    definable in an extension of linear temporal logic with unary numeric
    predicates and arithmetics.
LocCa: Visual Pretraining with Location-aware Captioners
Authors: Bo Wan; Michael Tschannen; Yongqin Xian; Filip Pavetic; Ibrahim Alabdulmohsin; Xiao Wang; André Susano Pinto; Andreas Peter Steiner; Lucas Beyer; Xiaohua Zhai
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Image captioning was recently found to be an effective pretraining method similar to contrastive pretraining. This opens up the largely-unexplored potential of using natural language as a flexible and powerful interface for handling diverse pretraining tasks. In this paper, we demonstrate this with a novel visual pretraining paradigm, LocCa, that incorporates location-aware tasks into captioners to teach models to extract rich information from images. Specifically, LocCa employs two tasks, bounding box prediction and location-dependent captioning, conditioned on the image pixel input. Thanks to the multitask capabilities of an encoder-decoder architecture, we show that an image captioner can effortlessly handle multiple tasks during pretraining. LocCa significantly outperforms standard captioners on downstream localization tasks, achieving state-of-the-art results on RefCOCO/+/g, while maintaining comparable performance on holistic tasks. Our work paves the way for further exploration of natural language interfaces in visual pretraining.
Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions
Authors: Wei Jiang; Sifan Yang; Yibo Wang; Lijun Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: This paper explores adaptive variance reduction methods for stochastic optimization based on the STORM technique. Existing adaptive extensions of STORM rely on strong assumptions like bounded gradients and bounded function values, or suffer an additional $\mathcal{O}(\log T)$ term in the convergence rate. To address these limitations, we introduce a novel adaptive STORM method that achieves an optimal convergence rate of $\mathcal{O}(T^{-1/3})$ for non-convex functions with our newly designed learning rate strategy. Compared with existing approaches, our method requires weaker assumptions and attains the optimal convergence rate without the additional $\mathcal{O}(\log T)$ term. We also extend the proposed technique to stochastic compositional optimization, obtaining the same optimal rate of $\mathcal{O}(T^{-1/3})$. Furthermore, we investigate the non-convex finite-sum problem and develop another innovative adaptive variance reduction method that achieves an optimal convergence rate of $\mathcal{O}(n^{1/4} T^{-1/2} )$, where $n$ represents the number of component functions. Numerical experiments across various tasks validate the effectiveness of our method.
Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection
Authors: Geng Yu; Jianing Zhu; Jiangchao Yao; Bo Han
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Out-of-distribution (OOD) detection is crucial for deploying reliable machine learning models in open-world applications. Recent advances in CLIP-based OOD detection have shown promising results via regularizing prompt tuning with OOD features extracted from ID data. However, the irrelevant context mined from ID data can be spurious due to the inaccurate foreground-background decomposition, thus limiting the OOD detection performance. In this work, we propose a novel framework, namely, \textit{Self-Calibrated Tuning (SCT)}, to mitigate this problem for effective OOD detection with only the given few-shot ID data. Specifically, SCT introduces modulating factors respectively on the two components of the original learning objective. It adaptively directs the optimization process between the two tasks during training on data with different prediction uncertainty to calibrate the influence of OOD regularization, which is compatible with many prompt tuning based OOD detection methods. Extensive experiments and analyses have been conducted to characterize and demonstrate the effectiveness of the proposed SCT. The code is publicly available at: https://github.com/tmlr-group/SCT.
Motion Forecasting in Continuous Driving
Authors: Nan Song; Bozhou Zhang; Xiatian Zhu; Li Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Motion forecasting for agents in autonomous driving is highly challenging due to the numerous possibilities for each agent's next action and their complex interactions in space and time. 
In real applications, motion forecasting takes place repeatedly and continuously as the self-driving car moves. However, existing forecasting methods typically process each driving scene within a certain range independently, totally ignoring the situational and contextual relationships between successive driving scenes. This significantly simplifies the forecasting task, making the solutions suboptimal and inefficient to use in practice. To address this fundamental limitation, we propose a novel motion forecasting framework for continuous driving, named RealMotion.
It comprises two integral streams both at the scene level:
(1) The scene context stream progressively accumulates historical scene information until the present moment, capturing temporal interactive relationships among scene elements.
(2) The agent trajectory stream optimizes current forecasting by sequentially relaying past predictions.
Besides, a data reorganization strategy is introduced to narrow the gap between existing benchmarks and real-world applications, consistent with our network. These approaches enable exploiting more broadly the situational and progressive insights of dynamic motion across space and time. 
Extensive experiments on Argoverse series with different settings demonstrate that our RealMotion achieves state-of-the-art performance, along with the advantage of efficient real-world inference.
Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction
Authors: Wei Jiang; Sifan Yang; Wenhao Yang; Lijun Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Sign stochastic gradient descent (signSGD) is a communication-efficient method that transmits only the sign of stochastic gradients for parameter updating. Existing literature has demonstrated that signSGD can achieve a convergence rate of $\mathcal{O}(d^{1/2}T^{-1/4})$, where $d$ represents the dimension and $T$ is the iteration number. In this paper, we improve this convergence rate to $\mathcal{O}(d^{1/2}T^{-1/3})$ by introducing the Sign-based Stochastic Variance Reduction (SSVR) method, which employs variance reduction estimators to track gradients and leverages their signs to update. For finite-sum problems, our method can be further enhanced to achieve a convergence rate of $\mathcal{O}(m^{1/4}d^{1/2}T^{-1/2})$, where $m$ denotes the number of component functions. Furthermore, we investigate the heterogeneous majority vote in distributed settings and introduce two novel algorithms that attain improved convergence rates of $\mathcal{O}(d^{1/2}T^{-1/2} + dn^{-1/2})$ and $\mathcal{O}(d^{1/4}T^{-1/4})$ respectively, outperforming the previous results of $\mathcal{O}(dT^{-1/4} + dn^{-1/2})$ and $\mathcal{O}(d^{3/8}T^{-1/8})$, where $n$ represents the number of nodes. Numerical experiments across different tasks validate the effectiveness of our proposed methods.
Mesa-Extrapolation: A Weave Position Encoding Method for Enhanced Extrapolation in LLMs
Authors: Xin Ma; Yang Liu; Jingjing Liu; Xiaoxu Ma
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Large language models (LLMs), although having revolutionized many fields, still suffer from the challenging extrapolation problem, where the inference ability of LLMs sharply declines beyond their max training lengths. In this work, we conduct a theoretical analysis to better understand why No Position Encoding (NoPE) fails outside its effective range, as well as examining the power of Position Encoding (PE) in this context. Our findings reveal that with meticulous weave position, PE can indeed be extended beyond effective range. Our theorems establish that LLMs equipped with weave PE can achieve improved extrapolation performance without additional cost. Furthermore, we introduce a novel weave PE method, Mesa-Extrapolation, which utilizes a chunk-based triangular attention matrix and applies Stair PE to manage the final chunk. This method not only retains competitive performance but also offers substantial benefits such as significantly reduced memory demand and faster inference speed. Extensive experiments validate the effectiveness of Mesa-Extrapolation, demonstrating its potential as a scalable solution to enhancing LLMs’ applicative reach.
Near-Optimality of Contrastive Divergence Algorithms
Authors: Pierre Glaser; Kevin Han Huang; Arthur Gretton
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We provide a non-asymptotic analysis of the contrastive divergence (CD) algorithm, a training method for unnormalized models. While prior work has established that (for exponential family distributions) the CD iterates asymptotically converge at an $O(n^{-1 / 3})$ rate to the true parameter of the data distribution, we show that CD can achieve the parametric rate $O(n^{-1 / 2})$. Our analysis provides results for various data batching schemes, including fully online and minibatch. We additionally show that CD is near-optimal, in the sense that its asymptotic variance is close to the Cramér-Rao lower bound.