GenAI4ELab/papercli-papers huggingface dataset:
The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning
Authors: Tian Jin; Nolan Clement; Xin Dong; Vaishnavh Nagarajan; Michael Carbin; Jonathan Ragan-Kelley; Gintare Karolina Dziugaite
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: We study how down-scaling large language model (LLM) size impacts LLM capabilities. We begin by measuring the effects of weight pruning – a popular technique for reducing model size – on the two abilities of LLMs: (a) recalling facts presented during pre-training and (b) processing information presented in context. Surprisingly, we find that existing pruning techniques affect these two abilities of LLMs differently. For example, pruning more than 30% of weights significantly decreases an LLM’s ability to recall facts presented during pre-training. Yet pruning 60-70% of weights largely preserves an LLM’s ability to process information in-context, ranging from retrieving answers based on information presented in context to learning parameterized functions such as a linear classifier based on a few examples. Moderate pruning impairs LLM’s ability to recall facts learnt from pre-training. However, its effect on model’s ability to process information presented in context is much less pronounced. The said disparate effects similarly arise when replacing the original model with a smaller dense one with reduced width and depth. This similarity suggests that model size reduction in general underpins the said disparity.
FedInverse: Evaluating Privacy Leakage in Federated Learning
Authors: Di Wu; Jun Bai; Yiliao Song; Junjun Chen; Wei Zhou; Yong Xiang; Atul Sajjanhar
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Federated Learning (FL) is a distributed machine learning technique where multiple devices (such as smartphones or IoT devices) train a shared global model by using their local data. FL claims that the data privacy of local participants is preserved well because local data will not be shared with either the server-side or other training participants. However, this paper discovers a pioneering finding that a model inversion (MI) attacker, who acts as a benign participant, can invert the shared global model and obtain the data belonging to other participants. This will lead to severe data-leakage risk in FL because it is difficult to identify attackers from benign participants.
In addition, we found even the most advanced defense approaches could not effectively address this issue. Therefore, it is important to evaluate such data-leakage risks of an FL system before using it. To alleviate this issue, we propose FedInverse to evaluate whether the FL global model can be inverted by MI attackers. In particular, FedInverse can be optimized by leveraging the Hilbert-Schmidt independence criterion (HSIC) as a regularizer to adjust the diversity of the MI attack generator. We test FedInverse with three typical MI attackers, GMI, KED-MI, and VMI, and the experiments show our FedInverse method can successfully obtain the data belonging to other participants. The code of this work is available at https://github.com/Jun-B0518/FedInverse
Constrained Bi-Level Optimization: Proximal Lagrangian Value Function Approach and Hessian-free Algorithm
Authors: Wei Yao; Chengming Yu; Shangzhi Zeng; Jin Zhang
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: This paper presents a new approach and algorithm for solving a class of constrained Bi-Level Optimization (BLO) problems in which the lower-level problem involves constraints coupling both upper-level and lower-level variables. Such problems have recently gained significant attention due to their broad applicability in machine learning. However, conventional gradient-based methods unavoidably rely on computationally intensive calculations related to the Hessian matrix. To address this challenge, we devise a smooth proximal Lagrangian value function to handle the constrained lower-level problem. Utilizing this construct, we introduce a single-level reformulation for constrained BLOs that transforms the original BLO problem into an equivalent optimization problem with smooth constraints. Enabled by this reformulation, we develop a Hessian-free gradient-based algorithm—termed proximal Lagrangian Value function-based Hessian-free Bi-level Algorithm (LV-HBA)—that is straightforward to implement in a single loop manner. Consequently, LV-HBA is especially well-suited for machine learning applications. Furthermore, we offer non-asymptotic convergence analysis for LV-HBA, eliminating the need for traditional strong convexity assumptions for the lower-level problem while also being capable of accommodating non-singleton scenarios. Empirical results substantiate the algorithm's superior practical performance.
Inherently Interpretable Time Series Classification via Multiple Instance Learning
Authors: Joseph Early; Gavin Cheung; Kurt Cutajar; Hanting Xie; Jas Kandola; Niall Twomey
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this work, we leverage Multiple Instance Learning (MIL) to overcome this issue, and propose a new framework called MILLET: Multiple Instance Learning for Locally Explainable Time series classification. We apply MILLET to existing deep learning TSC models and show how they become inherently interpretable without compromising (and in some cases, even improving) predictive performance. We evaluate MILLET on 85 UCR TSC datasets and also present a novel synthetic dataset that is specially designed to facilitate interpretability evaluation. On these datasets, we show MILLET produces sparse explanations quickly that are of higher quality than other well-known interpretability methods. To the best of our knowledge, our work with MILLET is the first to develop general MIL methods for TSC and apply them to an extensive variety of domains.
TOSS: High-quality Text-guided Novel View Synthesis from a Single Image
Authors: Yukai Shi; Jianan Wang; He CAO; Boshi Tang; Xianbiao Qi; Tianyu Yang; Yukun Huang; Shilong Liu; Lei Zhang; Heung-Yeung Shum
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: In this paper, we present TOSS, which introduces text to the task of novel view synthesis (NVS) from just a single RGB image. 
While Zero123 has demonstrated impressive zero-shot open-set NVS capabilities, it treats NVS as a pure image-to-image translation problem. This approach suffers from the challengingly under-constrained nature of single-view NVS: the process lacks means of explicit user control and often result in implausible NVS generations.
To address this limitation, TOSS uses text as high-level semantic information to constrain the NVS solution space.
TOSS fine-tunes text-to-image Stable Diffusion pre-trained on large-scale text-image pairs and introduces modules specifically tailored to image and camera pose conditioning, as well as dedicated training for pose correctness and preservation of fine details. 
Comprehensive experiments are conducted with results showing that our proposed TOSS outperforms Zero123 with higher-quality NVS results and faster convergence. We further support these results with comprehensive ablations that underscore the effectiveness and potential of 
the introduced semantic guidance and architecture design.
A Mutual Information Perspective on Federated Contrastive Learning
Authors: Christos Louizos; Matthias Reisser; Denis Korzhenkov
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: We investigate contrastive learning in the federated setting through the lens of Sim- CLR and multi-view mutual information maximization. In doing so, we uncover a connection between contrastive representation learning and user verification; by adding a user verification loss to each client’s local SimCLR loss we recover a lower bound to the global multi-view mutual information. To accommodate for the case of when some labelled data are available at the clients, we extend our SimCLR variant to the federated semi-supervised setting. We see that a supervised SimCLR objective can be obtained with two changes: a) the contrastive loss is computed between datapoints that share the same label and b) we require an additional auxiliary head that predicts the correct labels from either of the two views. Along with the proposed SimCLR extensions, we also study how different sources of non-i.i.d.-ness can impact the performance of federated unsupervised learning through global mutual information maximization; we find that a global objective is beneficial for some sources of non-i.i.d.-ness but can be detrimental for others. We empirically evaluate our proposed extensions in various tasks to validate our claims and furthermore demonstrate that our proposed modifications generalize to other pretraining methods.
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Authors: Xinyu Tang; Richard Shin; Huseyin A Inan; Andre Manoel; Fatemehsadat Mireshghallah; Zinan Lin; Sivakanth Gopi; Janardhan Kulkarni; Robert Sim
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. 
This scenario poses privacy risks, as LLMs may leak or regurgitate the private examples demonstrated in the prompt.
We propose a novel algorithm that generates synthetic few-shot demonstrations from the private dataset with formal differential privacy (DP) guarantees, and show empirically that it can achieve effective ICL.
We conduct extensive experiments on standard benchmarks and compare our algorithm with non-private ICL and zero-shot solutions. 
Our results demonstrate that our algorithm can achieve competitive performance with strong privacy levels.
These results open up new possibilities for ICL with privacy protection for a broad range of applications.
Grokking as a First Order Phase Transition in Two Layer Networks
Authors: Noa Rubin; Inbar Seroussi; Zohar Ringel
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: A key property of deep neural networks (DNNs) is their ability to learn new features during training. This intriguing aspect of deep learning stands out most clearly in recently reported Grokking phenomena. While mainly reflected as a sudden increase in test accuracy, Grokking is also believed to be a beyond lazy-learning/Gaussian Process (GP) phenomenon involving feature learning. Here we apply a recent development in the theory of feature learning, the adaptive kernel approach, to two teacher-student models with cubic-polynomial and modular addition teachers. We provide analytical predictions on feature learning and Grokking properties of these models and demonstrate a mapping between Grokking and the theory of phase transitions. We show that after Grokking, the state of the DNN is analogous to the mixed phase following a first-order phase transition. In this mixed phase, the DNN generates useful internal representations of the teacher that are sharply distinct from those before the transition.
Elucidating the Exposure Bias in Diffusion Models
Authors: Mang Ning; Mingxiao Li; Jianlin Su; Albert Ali Salah; Itir Onal Ertugrul
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Diffusion models have demonstrated impressive generative capabilities, but their exposure bias problem, described as the input mismatch between training and sampling, lacks in-depth exploration. In this paper, we investigate the exposure bias problem in diffusion models by first analytically modelling the sampling distribution, based on which we then attribute the prediction error at each sampling step as the root cause of the exposure bias issue. Furthermore, we discuss potential solutions to this issue and propose an intuitive metric for it. Along with the elucidation of exposure bias, we propose a simple, yet effective, training-free method called Epsilon Scaling to alleviate the exposure bias. We show that Epsilon Scaling explicitly moves the sampling trajectory closer to the vector field learned in the training phase by scaling down the network output, mitigating the input mismatch between training and sampling. Experiments on various diffusion frameworks (ADM, DDIM, EDM, LDM, DiT, PFGM++) verify the effectiveness of our method. Remarkably, our ADM-ES, as a state-of-the-art stochastic sampler, obtains 2.17 FID on CIFAR-10 under 100-step unconditional generation. The code is at https://github.com/forever208/ADM-ES
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise
Authors: Rui Pan; Yuxing Liu; Xiaoyu Wang; Tong Zhang
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Heavy-ball momentum with decaying learning rates is widely used with SGD for optimizing deep learning models. In contrast to its empirical popularity, the understanding of its theoretical property is still quite limited, especially under the standard anisotropic gradient noise condition for quadratic regression problems. Although it is widely conjectured that heavy-ball momentum method can provide accelerated convergence and should work well in large batch settings, there is no rigorous theoretical analysis. In this paper, we fill this theoretical gap by establishing a non-asymptotic convergence bound for stochastic heavy-ball methods with step decay scheduler on quadratic objectives, under the anisotropic gradient noise condition. As a direct implication, we show that heavy-ball momentum can provide $\tilde{\mathcal{O}}(\sqrt{\kappa})$ accelerated convergence of the bias term of SGD while still achieving near-optimal convergence rate with respect to the stochastic variance term. The combined effect implies an overall convergence rate within log factors from the statistical minimax rate. This means SGD with heavy-ball momentum is useful in the large-batch settings such as distributed machine learning or federated learning, where a smaller number of iterations can significantly reduce the number of communication rounds, leading to acceleration in practice.
Fast, Expressive $\mathrm{SE}(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space
Authors: Erik J Bekkers; Sharvaree Vadgama; Rob Hesselink; Putri A Van der Linden; David W. Romero
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Based on the theory of homogeneous spaces we derive *geometrically optimal edge attributes* to be used within the flexible message-passing framework. We formalize the notion of weight sharing in convolutional networks as the sharing of message functions over point-pairs that should be treated equally. We define equivalence classes of point-pairs that are identical up to a transformation in the group and derive attributes that uniquely identify these classes. Weight sharing is then obtained by conditioning message functions on these attributes. As an application of the theory, we develop an efficient equivariant group convolutional network for processing 3D point clouds. The theory of homogeneous spaces tells us how to do group convolutions with feature maps over the homogeneous space of positions $\mathbb{R}^3$, position and orientations $\mathbb{R}^3 {\times} S^2$, and the group $SE(3)$ itself. Among these, $\mathbb{R}^3 {\times} S^2$ is an optimal choice due to the ability to represent directional information, which $\mathbb{R}^3$ methods cannot, and it significantly enhances computational efficiency compared to indexing features on the full $SE(3)$ group. We support this claim with state-of-the-art results —in accuracy and speed— on five different benchmarks in 2D and 3D, including interatomic potential energy prediction, trajectory forecasting in N-body systems, and generating molecules via equivariant diffusion models.

*Code available at [https://github.com/ebekkers/ponita](https://github.com/ebekkers/ponita)*
Learning Large DAGs is Harder than you Think: Many Losses are Minimal for the Wrong DAG
Authors: Jonas Seng; Matej Zečević; Devendra Singh Dhami; Kristian Kersting
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Structure learning is a crucial task in science, especially in fields such as medicine and biology, where the wrong identification of (in)dependencies among random variables can have significant implications. The primary objective of structure learning is to learn a Directed Acyclic Graph (DAG) that represents the underlying probability distribution of the data. Many prominent DAG learners rely on least square losses or log-likelihood losses for optimization. It is well-known from regression models that least square losses are heavily influenced by the scale of the variables. Recently it has been demonstrated that the scale of data also affects performance of structure learning algorithms, though with a strong focus on linear 2-node systems and simulated data. Moving beyond these results, we provide conditions under which square-based losses are minimal for wrong DAGs in $d$-dimensional cases. Furthermore, we also show that scale can impair performance of structure learners if relations among variables are non-linear for both square based and log-likelihood based losses. We confirm our theoretical findings through extensive experiments on synthetic and real-world data.
An Analytical Solution to Gauss-Newton Loss for Direct Image Alignment
Authors: Sergei Solonets; Daniil Sinitsyn; Lukas Von Stumberg; Nikita Araslanov; Daniel Cremers
Venue: ICLR 2024 (ICLR 2024 oral)

Abstract: Direct image alignment is a widely used technique for relative 6DoF pose estimation between two images, but its accuracy strongly depends on pose initialization.
Therefore, recent end-to-end frameworks increase the convergence basin of the learned feature descriptors with special training objectives, such as the Gauss-Newton loss.
However, the training data may exhibit bias toward a specific type of motion and pose initialization,
thus limiting the generalization of these methods.
In this work, we derive a closed-form solution to the expected optimum of the Gauss-Newton loss. 
The solution is agnostic to the underlying feature representation and allows us to dynamically adjust the basin of convergence according to our assumptions about the uncertainty in the current estimates. These properties allow for effective control over the convergence in the alignment process.
Despite using self-supervised feature embeddings, our solution achieves compelling accuracy w.r.t. the state-of-the-art direct image alignment methods trained end-to-end with pose supervision, and demonstrates improved robustness to pose initialization.
Our analytical solution exposes some inherent limitations of end-to-end learning with the Gauss-Newton loss, and establishes an intriguing connection between direct image alignment and feature-matching approaches.
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling
Authors: Jingyang Zhang; Shiwei Li; Yuanxun Lu; Tian Fang; David Neil McKinnon; Yanghai Tsin; Long Quan; Yao Yao
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps). 
JointNet is extended from a pre-trained text-to-image diffusion model, where a copy of the original network is created for the new dense modality branch and is densely connected with the RGB branch. 
The RGB branch is locked during network fine-tuning, which enables efficient learning of the new modality distribution while maintaining the strong generalization ability of the large-scale pre-trained diffusion model.
We demonstrate the effectiveness of JointNet by using the RGB-D diffusion as an example and through extensive experiments, showcasing its applicability in a variety of applications, including joint RGB-D generation, dense depth prediction, depth-conditioned image generation, and high-resolution 3D panorama generation.
MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy
Authors: Yan Sun; Jicong Fan
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: This paper focuses on graph metric learning. First, we present a class of maximum mean discrepancy (MMD) based graph kernels, called MMD-GK. These kernels are computed by applying MMD to the node representations of two graphs with message-passing propagation. 
Secondly, we provide a class of deep MMD-GKs that are able to learn graph kernels and implicit graph features adaptively in an unsupervised manner. Thirdly, we propose a class of supervised deep MMD-GKs that are able to utilize label information of graphs and hence yield more discriminative metrics. Besides the algorithms, we provide theoretical analysis for the proposed methods. The proposed methods are evaluated in comparison to many baselines such as graph kernels and graph neural networks in the tasks of graph clustering and graph classification. The numerical results demonstrate the effectiveness and superiority of our methods.
Successor Heads: Recurring, Interpretable Attention Heads In The Wild
Authors: Rhys Gould; Euan Ong; George Ogden; Arthur Conmy
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: In this work we describe successor heads: attention heads that increment tokens with a natural ordering, such as numbers, months, and days.
For example, successor heads increment 'Monday' into 'Tuesday'.
We explain the successor head behavior with an approach rooted in mechanistic interpretability, the field that aims to explain how models complete tasks in human-understandable terms.
Existing research in this area has struggled to find recurring, mechanistically interpretable large language model (LLM) components beyond small toy models. Further, existing results have led to very little insight to explain the internals of the larger models that are used in practice.
In this paper, we analyze the behavior of successor heads in LLMs and find that they implement abstract representations that are common to different architectures. 
Successor heads form in LLMs with as few as 31 million parameters, and at least as many as 12 billion parameters, such as GPT-2, Pythia, and Llama-2.
We find a set of 'mod 10' features that underlie how successor heads increment in LLMs across different architectures and sizes.
We perform vector arithmetic with these features to edit head behavior and provide insights into numeric representations within LLMs. Additionally, we study the behavior of successor heads on natural language data, where we find that successor heads are important for achieving a low loss on examples involving succession, and also identify interpretable polysemanticity in a Pythia successor head.
Learning Energy Decompositions for Partial Inference in GFlowNets
Authors: Hyosoon Jang; Minsu Kim; Sungsoo Ahn
Venue: ICLR 2024 (ICLR 2024 oral)

Abstract: This paper studies generative flow networks (GFlowNets) to sample objects from the Boltzmann energy distribution via a sequence of actions. In particular, we focus on improving GFlowNet with partial inference: training flow functions with the evaluation of the intermediate states or transitions. To this end, the recently developed forward-looking GFlowNet reparameterizes the flow functions based on evaluating the energy of intermediate states. However, such an evaluation of intermediate energies may (i) be too expensive or impossible to evaluate and (ii) even provide misleading training signals under large energy fluctuations along the sequence of actions. To resolve this issue, we propose learning energy decompositions for GFlowNets (LED-GFN). Our main idea is to (i) decompose the energy of an object into learnable potential functions defined on state transitions and (ii) reparameterize the flow functions using the potential functions. In particular, to produce informative local credits, we propose to regularize the potential to change smoothly over the sequence of actions. It is also noteworthy that training GFlowNet with our learned potential can preserve the optimal policy. We empirically verify the superiority of LED-GFN in five problems including the generation of unstructured and maximum independent sets, molecular graphs, and RNA sequences.