GenAI4ELab/papercli-papers huggingface dataset:
ASID: Active Exploration for System Identification in Robotic Manipulation
Authors: Marius Memmel; Andrew Wagenmaker; Chuning Zhu; Dieter Fox; Abhishek Gupta
Venue: ICLR 2024 (ICLR 2024 oral)

Abstract: Model-free control strategies such as reinforcement learning have shown the ability to learn control strategies without requiring an accurate model or simulator of the world. While this is appealing due to the lack of modeling requirements, such methods can be sample inefficient, making them impractical in many real-world domains. On the other hand, model-based control techniques leveraging accurate simulators can circumvent these challenges and use a large amount of cheap simulation data to learn controllers that can effectively transfer to the real world. The challenge with such model-based techniques is the requirement for an extremely accurate simulation, requiring both the specification of appropriate simulation assets and physical parameters. This requires considerable human effort to design for every environment being considered. In this work, we propose a learning system that can leverage a small amount of real-world data to autonomously refine a simulation model and then plan an accurate control strategy that can be deployed in the real world. Our approach critically relies on utilizing an initial (possibly inaccurate) simulator to design effective exploration policies that, when deployed in the real world, collect high-quality data. We demonstrate the efficacy of this paradigm in identifying articulation, mass, and other physical parameters in several challenging robotic manipulation tasks, and illustrate that only a small amount of real-world data can allow for effective sim-to-real transfer.
Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks
Authors: Xihaier Luo; Wei Xu; Balu Nadiga; Yihui Ren; Shinjae Yoo
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Reliably reconstructing physical fields from sparse sensor data is a challenge that frequenty arises in many scientific domains. In practice, the process generating the data is often not known to sufficient accuracy. Therefore, there is a growing interest in the deep neural network route to the problem. In this work, we present a novel approach that learns a continuous representation of the field using implicit neural representations (INR). Specifically, after factorizing spatiotemporal variability into spatial and temporal components using the technique of separation of variables, the method learns relevant basis functions from sparsely sampled irregular data points to thus develop a continuous representation of the data. In experimental evaluations, the proposed model outperforms recent INR methods, offering superior reconstruction quality on simulation data from a state of the art climate model and on a second dataset that comprises of ultra-high resolution satellite-based sea surface temperature field. [Website for the Project: Both data and code are accessible.](https://xihaier.github.io/ICLR-2024-MMGN/)
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models
Authors: Yefei He; Jing Liu; Weijia Wu; Hong Zhou; Bohan Zhuang
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Diffusion models have demonstrated remarkable capabilities in image synthesis and related generative tasks. Nevertheless, their practicality for low-latency real-world applications is constrained by substantial computational costs and latency issues. Quantization is a dominant way to compress and accelerate diffusion models, where post-training quantization (PTQ) and quantization-aware training (QAT) are two main approaches, each bearing its own properties. While PTQ exhibits efficiency in terms of both time and data usage, it may lead to diminished performance in low bit-width settings. On the other hand, QAT can help alleviate performance degradation but comes with substantial demands on computational and data resources. To capitalize on the advantages while avoiding their respective drawbacks, we introduce a data-free, quantization-aware and parameter-efficient fine-tuning framework for low-bit diffusion models, dubbed EfficientDM, to achieve QAT-level performance with PTQ-like efficiency. Specifically, we propose a quantization-aware variant of the low-rank adapter (QALoRA) that can be merged with model weights and jointly quantized to low bit-width. The fine-tuning process distills the denoising capabilities of the full-precision model into its quantized counterpart, eliminating the requirement for training data. To further enhance performance, we introduce scale-aware optimization to address ineffective learning of QALoRA due to variations in weight quantization scales across different layers. We also employ temporal learned step-size quantization to handle notable variations in activation distributions across denoising steps. Extensive experimental results demonstrate that our method significantly outperforms previous PTQ-based diffusion models while maintaining similar time and data efficiency. Specifically, there is only a marginal $0.05$ sFID increase when quantizing both weights and activations of LDM-4 to 4-bit on ImageNet $256\times256$. Compared to QAT-based methods, our EfficientDM also boasts a $16.2\times$ faster quantization speed with comparable generation quality, rendering it a compelling choice for practical applications.
Weaker MVI Condition: Extragradient Methods with Multi-Step Exploration
Authors: Yifeng Fan; Yongqiang Li; Bo Chen
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: This paper proposes a new framework of algorithms that is extended from the celebrated extragradient algorithm. The min-max problem has attracted increasing attention because of its applications in machine learning tasks such as generative adversarial networks (GANs) training. While there has been exhaustive research on convex-concave setting, problem of nonconvex-nonconcave setting faces many challenges, such as convergence to limit cycles. Given that general min-max optimization has been found to be intractable, recent research efforts have shifted towards tackling structured problems. One of these follows the weak Minty variational inequality (weak MVI), which is motivated by relaxing Minty variational inequality (mvi) without compromising convergence guarantee of extragradient algorithm. Existing extragradient-type algorithms involve one exploration step and one update step per iteration. We analyze the algorithms with multiple exploration steps and show that current assumption can be further relaxed when more exploration is introduced. Furthermore, we design an adaptive algorithm that explores until the optimal improvement is achieved. This process exploits information from the whole trajectory and effectively tackles cyclic behaviors.
BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models
Authors: Qingqing Cao; Sewon Min; Yizhong Wang; Hannaneh Hajishirzi
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Retrieval augmentation addresses many critical problems in large language models such as hallucination, staleness, and privacy leaks.
However, running retrieval-augmented language models (LMs) is slow and difficult to scale due to processing large amounts of retrieved text. 
We introduce binary token representations (BTR), which use 1-bit vectors to precompute every token in passages, significantly reducing computation during inference. 
Despite the potential loss of accuracy, our new calibration techniques and training objectives restore performance. Combined with offline and runtime compression, this only requires 127GB of disk space for encoding 3 billion tokens in Wikipedia.
Our experiments show that on five knowledge-intensive NLP tasks, BTR accelerates state-of-the-art inference by up to 4x and reduces storage by over 100x while maintaining over 95% task performance. Our code is publicly available at https://github.com/csarron/BTR.
Demystifying Embedding Spaces using Large Language Models
Authors: Guy Tennenholtz; Yinlam Chow; ChihWei Hsu; Jihwan Jeong; Lior Shani; Azamat Tulepbergenov; Deepak Ramachandran; Martin Mladenov; Craig Boutilier
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format. Nevertheless, they often preclude direct interpretation. While downstream tasks make use of these compressed representations, meaningful interpretation usually requires visualization using dimensionality reduction or specialized machine learning interpretability methods. This paper addresses the challenge of making such embeddings more interpretable and broadly useful, by employing large language models (LLMs) to directly interact with embeddings -- transforming abstract vectors into understandable narratives. By injecting embeddings into LLMs, we enable querying and exploration of complex embedding data. We demonstrate our approach on a variety of diverse tasks, including: enhancing concept activation vectors (CAVs), communicating novel embedded entities, and decoding user preferences in recommender systems. Our work couples the immense information potential of embeddings with the interpretative power of LLMs.
The Expressive Power of Low-Rank Adaptation
Authors: Yuchen Zeng; Kangwook Lee
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: *Low-Rank Adaptation* (LoRA), a parameter-efficient fine-tuning method that leverages low-rank adaptation of weight matrices, has emerged as a prevalent technique for fine-tuning pre-trained models such as large language models and diffusion models.
Despite its huge success in practice, the theoretical underpinnings of LoRA have largely remained unexplored. 
This paper takes the first step to bridge this gap by theoretically analyzing the expressive power of LoRA. 
We prove that, for fully connected neural networks, LoRA can adapt any model $f$ to accurately represent any smaller target model $\bar{f}$ if LoRA-rank $\geq(\text{width of }f) \times \frac{\text{depth of }\bar{f}}{\text{depth of }f}$, under a mild assumption. 
We also quantify the approximation error when the LoRA-rank is lower than the threshold. 
For Transformer networks, we show any model can be adapted to a target model of the same size with rank-$(\frac{\text{embedding size}}{2})$ LoRA adapters.
All our theoretical insights are validated by numerical experiments.
Towards Category Unification of 3D Single Object Tracking on Point Clouds
Authors: Jiahao Nie; Zhiwei He; Xudong Lv; Xueyi Zhou; Dong-Kyu Chae; Fei Xie
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Category-specific models are provenly valuable methods in 3D single object tracking (SOT) regardless of Siamese or motion-centric paradigms. However, such over-specialized model designs incur redundant parameters, thus limiting the broader applicability of 3D SOT task. This paper first introduces unified models that can simultaneously track objects across all categories using a single network with shared model parameters. Specifically, we propose to explicitly encode distinct attributes associated to different object categories, enabling the model to adapt to cross-category data. We find that the attribute variances of point cloud objects primarily occur from the varying size and shape (e.g., large and square vehicles v.s. small and slender humans). Based on this observation, we design a novel point set representation learning network inheriting transformer architecture, termed AdaFormer, which adaptively encodes the dynamically varying shape and size information from cross-category data in a unified manner. We further incorporate the size and shape prior derived from the known template targets into the model’s inputs and learning objective, facilitating the learning of unified representation. Equipped with such designs, we construct two category-unified models SiamCUT and MoCUT. Extensive experiments demonstrate that SiamCUT and MoCUT exhibit strong generalization and training stability. Furthermore, our category-unified models outperform the category-specific counterparts by a significant margin (e.g., on KITTI dataset, $\sim$12\% and $\sim$3\% performance gains on the Siamese and motion paradigms).
You Only Query Once: An Efficient Label-Only Membership Inference Attack
Authors: YUTONG WU; Han Qiu; Shangwei Guo; Jiwei Li; Tianwei Zhang
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: As one of the privacy threats to machine learning models, the membership inference attack (MIA) tries to infer whether a given sample is in the original training set of a victim model by analyzing its outputs. Recent studies only use the predicted hard labels to achieve impressive membership inference accuracy. However, such label-only MIA approach requires very high query budgets to evaluate the distance of the target sample from the victim model's decision boundary.  
   We propose YOQO, a novel label-only attack to overcome the above limitation.YOQO aims at identifying a special area (called improvement area) around the target sample and crafting a query sample, whose hard label from the victim model can reliably reflect the target sample's membership. YOQO can successfully reduce the query budget from more than 1,000 times to only ONCE. Experiments demonstrate that YOQO is not only as effective as SOTA attack methods, but also performs comparably or even more robustly against many sophisticated defenses.
Frozen Transformers in Language Models Are Effective Visual Encoder Layers
Authors: Ziqi Pang; Ziyang Xie; Yunze Man; Yu-Xiong Wang
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: This paper reveals that large language models (LLMs), despite being trained solely on text data, are surprisingly}strong encoders for purely visual tasks in the absence of language. Even more intriguingly, this can be achieved by a simple yet previously overlooked strategy -- employing a frozen transformer block from pre-trained LLMs as a constituent encoder layer to directly process visual tokens. Our work pushes the boundaries of leveraging LLMs for computer vision tasks, significantly departing from conventional practices that typically necessitate a multi-modal vision-language setup with associated language prompts, inputs, or outputs. We demonstrate that our approach consistently enhances performance across a diverse range of tasks} encompassing pure 2D or 3D visual recognition tasks (e.g., image and point cloud classification), temporal modeling tasks (e.g., action recognition), non-semantic tasks (e.g., motion forecasting), and multi-modal tasks (e.g., 2D/3D visual question answering and image-text retrieval). Such improvements are a general phenomenon, applicable to various types of LLMs (e.g., LLaMA and OPT) and different LLM transformer blocks. We additionally propose the information filtering hypothesis to explain the effectiveness of pre-trained LLMs in visual encoding -- the pre-trained LLM transformer blocks discern informative visual tokens and further amplify their effect. This hypothesis is empirically supported by the observation that the feature activation, after training with LLM transformer blocks, exhibits a stronger focus on relevant regions. We hope that our work inspires new perspectives on utilizing LLMs and deepening our understanding of their underlying mechanisms.
Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem
Authors: Albert Xu; Jhih-Yi Hsieh; Bhaskar Vundurthy; Nithya Kemp; Eliana Cohen; Lu Li; Howie Choset
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: In deep metric learning, the triplet loss has emerged as a popular method to learn many computer vision and natural language processing tasks such as facial recognition, object detection, and visual-semantic embeddings. One issue that plagues the triplet loss is network collapse, an undesirable phenomenon where the network projects the embeddings of all data onto a single point. Researchers predominately solve this problem by using triplet mining strategies. While hard negative mining is the most effective of these strategies, existing formulations lack strong theoretical justification for their empirical success. In this paper, we utilize the mathematical theory of isometric approximation to show an equivalence between the triplet loss sampled by hard negative mining and an optimization problem that minimizes a Hausdorff-like distance between the neural network and its ideal counterpart function. This provides the theoretical justifications for hard negative mining's empirical efficacy. Experiments performed on the Market-1501 and Stanford Online Products datasets with various network architectures corroborate our theoretical findings, indicating that network collapse tends to happen when batch size is too large or embedding dimension is too small. In addition, our novel application of the isometric approximation theorem provides the groundwork for future forms of hard negative mining that avoid network collapse.
Bridging Neural and Symbolic Representations with Transitional Dictionary Learning
Authors: Junyan Cheng; Peter Chin
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: This paper introduces a novel Transitional Dictionary Learning (TDL) framework that can implicitly learn symbolic knowledge, such as visual parts and relations, by reconstructing the input as a combination of parts with implicit relations. We propose a game-theoretic diffusion model to decompose the input into visual parts using the dictionaries learned by the Expectation Maximization (EM) algorithm, implemented as the online prototype clustering, based on the decomposition results. Additionally, two metrics, clustering information gain, and heuristic shape score are proposed to evaluate the model. Experiments are conducted on three abstract compositional visual object datasets, which require the model to utilize the compositionality of data instead of simply exploiting visual features.  Then, three tasks on symbol grounding to predefined classes of parts and relations, as well as transfer learning to unseen classes, followed by a human evaluation, were carried out on these datasets. The results show that the proposed method discovers compositional patterns, which significantly outperforms the state-of-the-art unsupervised part segmentation methods that rely on visual features from pre-trained backbones. Furthermore, the proposed metrics are consistent with human evaluations.
AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
Authors: Kethmi Hirushini Hettige; Jiahao Ji; Shili Xiang; Cheng Long; Gao Cong; Jingyuan Wang
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Air quality prediction and modelling plays a pivotal role in public health and environment management, for individuals and authorities to make informed decisions. Although traditional data-driven models have shown promise in this domain, their long-term prediction accuracy can be limited, especially in scenarios with sparse or incomplete data and they often rely on black-box deep learning structures that lack solid physical foundation leading to reduced transparency and interpretability in predictions. To address these limitations, this paper presents a novel approach named Physics guided Neural Network for Air Quality Prediction (AirPhyNet). Specifically, we leverage two well-established physics principles of air particle movement (diffusion and advection) by representing them as differential equation networks. Then, we utilize a graph structure to integrate physics knowledge into a neural network architecture and exploit latent representations to capture spatio-temporal relationships within the air quality data. Experiments on two real-world benchmark datasets demonstrate that AirPhyNet outperforms state-of-the-art models for different testing scenarios including different lead time (24h, 48h, 72h), sparse data and sudden change prediction, achieving reduction in prediction errors up to 10\%. Moreover, a case study further validates that our model captures underlying physical processes of particle movement and generates accurate predictions with real physical meaning. The code is available at: https://github.com/kethmih/AirPhyNet
SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series
Authors: Junyan Cheng; Peter Chin
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: We introduce SocioDojo, an open-ended lifelong learning environment for developing ready-to-deploy autonomous agents capable of performing human-like analysis and decision-making on societal topics such as economics, finance, politics, and culture. It consists of (1) information sources from news, social media, reports, etc., (2) a knowledge base built from books, journals, and encyclopedias, plus a toolbox of Internet and knowledge graph search interfaces, (3) 30K high-quality time series in finance, economy, society, and polls, which support a novel task called "hyperportfolio", that can reliably and scalably evaluate societal analysis and decision-making power of agents, inspired by portfolio optimization with time series as assets to "invest". We also propose a novel Analyst-Assistant-Actuator architecture for the hyperportfolio task, and a Hypothesis & Proof prompting for producing in-depth analyses on input news, articles, etc. to assist decision-making. We perform experiments and ablation studies to explore the factors that impact performance. The results show that our proposed method achieves improvements of 32.4% and 30.4% compared to the state-of-the-art method in the two experimental settings.
DittoGym: Learning to Control Soft Shape-Shifting Robots
Authors: Suning Huang; Boyuan Chen; Huazhe Xu; Vincent Sitzmann
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Robot co-design, where the morphology of a robot is optimized jointly with a learned policy to solve a specific task, is an emerging area of research. It holds particular promise for soft robots, which are amenable to novel manufacturing techniques that can realize learned morphologies and actuators. Inspired by nature and recent novel robot designs, we propose to go a step further and explore the novel reconfigurable robots, defined as robots that can change their morphology within their lifetime. We formalize control of reconfigurable soft robots as a high-dimensional reinforcement learning (RL) problem. We unify morphology change, locomotion, and environment interaction in the same action space, and introduce an appropriate, coarse-to-fine curriculum that enables us to discover policies that accomplish fine-grained control of the resulting robots. We also introduce DittoGym, a comprehensive RL benchmark for reconfigurable soft robots that require fine-grained morphology changes to accomplish the tasks. Finally, we evaluate our proposed coarse-to-fine algorithm on DittoGym,  and demonstrate robots that learn to change their morphology several times within a sequence, uniquely enabled by our RL algorithm. More results are available at https://dittogym.github.io.