GenAI4ELab/papercli-papers huggingface dataset:
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation
Authors: Lorenz Kuhn; Yarin Gal; Sebastian Farquhar
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of foundation models. We show that measuring uncertainty in natural language is challenging because of "semantic equivalence"—different sentences can mean the same thing. To overcome these challenges we introduce semantic entropy—an entropy which incorporates linguistic invariances created by shared meanings. Our method is unsupervised, uses only a single model, and requires no modifications to off-the-shelf language models. In comprehensive ablation studies we show that the semantic entropy is more predictive of model accuracy on question answering data sets than comparable baselines.
A Message Passing Perspective on Learning Dynamics of Contrastive Learning
Authors: Yifei Wang; Qi Zhang; Tianqi Du; Jiansheng Yang; Zhouchen Lin; Yisen Wang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In recent years, contrastive learning achieves impressive results on self-supervised visual representation learning, but there still lacks a rigorous understanding of its learning dynamics. In this paper, we show that if we cast a contrastive objective equivalently into the feature space, then its learning dynamics admits an interpretable form. Specifically, we show that its gradient descent corresponds to a specific message passing scheme on the corresponding augmentation graph. Based on this perspective, we theoretically characterize how contrastive learning gradually learns discriminative features with the alignment update and the uniformity update. Meanwhile, this perspective also establishes an intriguing connection between contrastive learning and Message Passing Graph Neural Networks (MP-GNNs). This connection not only provides a unified understanding of many techniques independently developed in each community, but also enables us to borrow techniques from MP-GNNs to design new contrastive learning variants, such as graph attention, graph rewiring, jumpy knowledge techniques, etc. We believe that our message passing perspective not only provides a new theoretical understanding of contrastive learning dynamics, but also bridges the two seemingly independent areas together, which could inspire more interleaving studies to benefit from each other. The code is available at https://github.com/PKU-ML/Message-Passing-Contrastive-Learning.
SmilesFormer: Language Model for Molecular Design
Authors: Joshua Owoyemi; Nazim Medzhidov
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The objective of drug discovery is to find novel compounds with desirable chemical properties. Generative models have been utilized to sample molecules at the intersection of multiple property constraints. In this paper we pose molecular design as a language modeling problem where the model implicitly learns the vocabulary and composition of valid molecules, hence it is able to generate new molecules of interest. We present SmilesFormer, a Transformer-based model which is able to encode molecules, molecule fragments, and fragment compositions as latent variables, which are in turn decoded to stochastically generate novel molecules. This is achieved by fragmenting the molecules into smaller combinatorial groups, then learning the mapping between the input fragments and valid SMILES sequences. 
The model is able to optimize molecular properties through a stochastic latent space traversal technique. This technique systematically searches the encoded latent space to find latent vectors that are able to produce molecules to meet the multi-property objective. The model was validated through various de novo molecular design tasks, achieving state-of-the-art performances when compared to previous methods. Furthermore, we used the proposed method to demonstrate a drug rediscovery pipeline for Donepezil, a known Acetylcholinesterase Inhibitor.
Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body
Authors: Justice Mason; Christine Allen-Blanchette; Nicholas F Zolman; Elizabeth Davison; Naomi Leonard
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In many real-world settings, image observations of freely rotating 3D rigid bodies, such as satellites, may be available when low-dimensional measurements are not. However, the high-dimensionality of image data precludes the use of classical estimation techniques to learn the dynamics and a lack of interpretability reduces the usefulness of standard deep learning methods. In this work, we present a physics-informed neural network model to estimate and predict 3D rotational dynamics from image sequences. We achieve this using a multi-stage prediction pipeline that maps individual images to a latent representation homeomorphic to $\mathbf{SO}(3)$, computes angular velocities from latent pairs, and predicts future latent states using the Hamiltonian equations of motion with a learned representation of the Hamiltonian. We demonstrate the efficacy of our approach on a new rotating rigid-body dataset with sequences of rotating cubes and rectangular prisms with uniform and non-uniform density.
BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers
Authors: Zhiliang Peng; Li Dong; Hangbo Bao; Qixiang Ye; Furu Wei
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Masked image modeling (MIM) has demonstrated impressive results in self-supervised representation learning by recovering corrupted image patches. However, most existing studies operate on low-level image pixels, which hinders the exploitation of high-level semantics for representation models. In this work, we propose to use a semantic-rich visual tokenizer as the reconstruction target for masked prediction, providing a systematic way to promote MIM from pixel-level to semantic-level. Specifically, we propose vector-quantized knowledge distillation to train the tokenizer, which discretizes a continuous semantic space to compact codes. We then pretrain vision Transformers by predicting the original visual tokens for the masked image patches. Furthermore, we introduce a patch aggregation strategy which associates discrete image patches to enhance global semantic representation. Experiments on image classification and semantic segmentation show that BEiT v2 outperforms all compared MIM methods. On ImageNet-1K (224 size), the base-size BEiT v2 achieves $85.5\%$ top-1 accuracy for fine-tuning and $80.1\%$ top-1 accuracy for linear probing. The large-size BEiT v2 obtains $87.3\%$ top-1 accuracy for ImageNet-1K (224 size) fine-tuning, and $56.7\%$ mIoU on ADE20K for semantic segmentation. The code can be found in the supplementary materials.
DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity
Authors: Alexander Tyurin; Peter Richtárik
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We develop and analyze  DASHA: a new family of methods for nonconvex distributed optimization problems. When the local functions at the nodes have a finite-sum or an expectation form, our new methods, DASHA-PAGE, DASHA-MVR and DASHA-SYNC-MVR, improve the theoretical oracle and communication complexity of the previous state-of-the-art method MARINA by Gorbunov et al. (2020). In particular, to achieve an $\varepsilon$-stationary point, and considering the random sparsifier Rand$K$ as an example, our methods compute the optimal number of gradients $\mathcal{O}\left(\frac{\sqrt{m}}{\varepsilon\sqrt{n}}\right)$ and $\mathcal{O}\left(\frac{\sigma}{\varepsilon^{3/2}n}\right)$ in finite-sum and expectation form cases, respectively, while maintaining the SOTA communication complexity $\mathcal{O}\left(\frac{d}{\varepsilon \sqrt{n}}\right)$. Furthermore, unlike MARINA, the new methods DASHA, DASHA-PAGE and DASHA-MVR send compressed vectors only, which makes them more practical for federated learning. We extend our results to the case when the functions satisfy the Polyak-Lojasiewicz condition. Finally, our theory is corroborated in practice: we see a significant improvement in experiments with nonconvex classification and training of deep learning models.
Identification of the Adversary from a Single Adversarial Example
Authors: Minhao Cheng; Rui Min; Haochen Sun
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Deep neural networks have been shown vulnerable to adversarial examples. Even though many defence methods have been proposed to enhance the robustness, it is still a long way toward providing an attack-free method to build a trustworthy machine learning system. In this paper, instead of enhancing the robustness, we take the investigator's perspective and propose a new framework to trace the first compromised model in a forensic investigation manner. Specifically, we focus on the following setting: the machine learning service provider provides models for a set of customers. However, one of the customers conducted adversarial attacks to fool the system. Therefore, the investigator's objective is to identify the first compromised model by collecting and analyzing evidence from only available adversarial examples. To make the tracing viable, we design a random mask watermarking mechanism to differentiate adversarial examples from different models. First, we propose a tracing approach in the data-limited case where the original example is also available. Then, we design a data-free approach to identify the adversary without accessing the original example. Finally, the effectiveness of our proposed framework is evaluated by extensive experiments with different model architectures, adversarial attacks, and datasets.
CroMA: Cross-Modality Adaptation for Monocular BEV Perception
Authors: Yunze Man; Liangyan Gui; Yu-Xiong Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Incorporating multiple sensor modalities, and closing the domain gaps between training and deployment are two challenging yet critical topics for self-driving. Existing adaption works only focus on visual-level domain gaps, overlooking the sensor-type gaps which exist in reality. A model trained with a collection of sensor modalities may need to run on another setting with less types of sensors available.  In this work, we propose a Cross-Modality Adaptation (CroMA) framework to facilitate the learning of a more robust monocular BEV perception model, which transfer the point clouds knowledge from LiDAR sensor during training phase to the camera-only testing scenario. The absence of LiDAR during testing negates the usage of it as model input. Hence, our key idea lies in the design of a LiDAR-teacher and Camera-student knowledge distillation model, as well as a multi-level adversarial learning mechanism, which adapt and align the features learned from different sensors and domains. This work results in the first open analysis of cross-domain perception and cross-sensor adaptation model for monocular 3D tasks in the wild. We benchmark our approach on large-scale datasets under various domain shifts and show state-of-the-art results against various baselines.
Towards Learning Implicit Symbolic Representation for Visual Reasoning
Authors: Chen Sun; Calvin Luo; Xingyi Zhou; Anurag Arnab; Cordelia Schmid
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Visual reasoning tasks are designed to test a learning algorithm's capability to infer causal relationships, discover object interactions, and understand temporal dynamics, all from visual cues. It is commonly believed that to achieve compositional generalization on visual reasoning, an explicit abstraction of the visual scene must be constructed; for example, object detection can be applied to the visual input to produce representations that are then processed by a neural network or a neuro-symbolic framework. We demonstrate that a simple and general self-supervised approach is able to learn implicit symbolic representations with general-purpose neural networks, enabling the end-to-end learning of visual reasoning directly from raw visual inputs. Our proposed approach ``compresses'' each frame of a video into a small set of tokens with a transformer network. The self-supervised learning objective is to reconstruct each image based on the compressed temporal context. To minimize the reconstruction loss, the network must learn a compact representation for each image, as well as capture temporal dynamics and object permanence from temporal context. We evaluate the proposed approach on two visual reasoning benchmarks, CATER and ACRE. We observe that self-supervised pretraining is essential to achieve compositional generalization for our end-to-end trained neural network, and our proposed method achieves on par or better performance compared to recent neuro-symbolic approaches that often require additional object-level supervision.
Bias Propagation in Federated Learning
Authors: Hongyan Chang; Reza Shokri
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We show that participating in federated learning can be detrimental to group fairness. In fact, the bias of a few parties against under-represented groups (identified by sensitive attributes such as gender or race) can propagate through the network to all the parties in the network. We analyze and explain bias propagation in federated learning on naturally partitioned real-world datasets. Our analysis reveals that biased parties unintentionally yet stealthily encode their bias in a small number of model parameters, and throughout the training, they steadily increase the dependence of the global model on sensitive attributes. What is important to highlight is that the experienced bias in federated learning is higher than what parties would otherwise encounter in centralized training with a model trained on the union of all their data. This indicates that the bias is due to the algorithm. Our work calls for auditing group fairness in federated learning and designing learning algorithms that are robust to bias propagation.
Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size
Authors: Alexander Nikulin; Vladislav Kurenkov; Denis Tarasov; Dmitry Akimov; Sergey Kolesnikov
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Training large neural networks is known to be time-consuming, where learning duration may stretch up to days or weeks. To address this problem, the approach of large-batch optimization was introduced, demonstrating that scaling mini-batch sizes with appropriate learning rate adjustments may speed up the training process by orders of magnitude. While long training time was not typically a major issue for model-free deep offline RL algorithms, recently introduced Q-ensemble methods achieving state-of-the-art performance made this issue more relevant, notably extending the training duration. In this work, we demonstrate how large-batch optimization, typically overlooked in deep offline RL community, can benefit this class of methods. We show that simply scaling the mini-batch size and naively adjusting the learning rate allows for (1) a reduced size of the Q-ensemble, (2) stronger penalization of out-of-distribution actions, and (3) improved convergence time, effectively shortening training durations by 2.5x times on average.
Learning Arborescence with An Efficient Inference Algorithm
Authors: Nan Jiang; Maxwell J Jacobson; Yexiang Xue
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We consider a class of structured learning problems on arborescence (i.e., the directed spanning tree) from the input graph. The key step involved in this problem is predicting the minimal weight arborescence (MWA) from the learned model. In literature, there are two lines of research for predicting MWA: the Chu-Liu Edmonds (CLE) and the Lovasz methods.  The CLE method is easy to implement while it takes $\mathcal{O}(n)$ cycle contractions. Here $n$ is the graph size.  The Lovasz method reduces to the multi-pair shortest path (MPSP) problem and takes only $\mathcal{O}(\log n)$ contractions. Nevertheless, in the CPU setting, MPSP has the same time complexity as finding MWA. The Lovasz method only attains time efficiency under a sufficient GPU setting. Both the aforementioned methods are painfully slow for large-scale learning tasks.  In this research, we find the general MPSP problem can be simplified when working with machine learning models. This is because the learning model predicts edge weights for all pairs of vertices and the graph we process is always complete.  Therefore, we only need to handle those paths that directly enter every weakly connected component (WCC) while the classic Lovasz method needs to handle all possible paths. This allows us to propose LAzy LoVAz (Lava) method that enjoys $\mathcal{O}(\log n)$ contractions as well as efficient performance in both CPU and GPU settings. In experiments, we consider synthetic datasets and two real-world learning tasks, i.e., graph-based dependency parsing and unsupervised parsing on ListOps.  The empirical results exhibit important gains of our Lava method to the classic CLE and Lovasz methods, that Lava boosts the training time for arborescence learning tasks.
How Hard is Trojan Detection in DNNs? Fooling Detectors With Evasive Trojans
Authors: Mantas Mazeika; Andy Zou; Akul Arora; Pavel Pleskov; Dawn Song; Dan Hendrycks; Bo Li; David Forsyth
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: As AI systems become more capable and widely used, a growing concern is the possibility for trojan attacks in which adversaries inject deep neural networks with hidden functionality. Recently, methods for detecting trojans have proven surprisingly effective against existing attacks. However, there is comparatively little work on whether trojans themselves could be rendered hard to detect. To fill this gap, we develop a general method for making trojans more evasive based on several novel techniques and observations. Our method combines distribution-matching, specificity, and randomization to eliminate distinguishing features of trojaned networks. Importantly, our method can be applied to various existing trojan attacks and is detector-agnostic. In experiments, we find that our evasive trojans reduce the efficacy of a wide range of detectors across numerous evaluation settings while maintaining high attack success rates. Moreover, we find that evasive trojans are also harder to reverse-engineer, underscoring the importance of developing more robust monitoring mechanisms for neural networks and clarifying the offence-defense balance of trojan detection.
Vectorial Graph Convolutional Networks
Authors: ZhongYu Li; Geng Zhao; Hao Ning
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract:    Graph Convolutional Networks (GCN) have drawn considerable attention recently due to their outstanding performance in processing graph-structured data. However, GCNs still limited to the undirected graph because they theoretically require a symmetric matrix as the basis for the Laplacian transform. This causes the isotropic problem of the operator and reduced sensitivity in response to different information. In order to solve the problem, we generalize the spectral convolution operator to directed graphs by field extension, which improves the edge representations from scalars to vectors. Therefore, it brings in the concept of direction. That is to say, and even homogeneous information can become distinguishable by its differences in directions.In this paper, we propose the Vectorial Graph Convolutional Network(VecGCN) and the experimental evidence showing the advantages of a variety of directed graph node classification and link prediction tasks.
Causal Mean Field Multi-Agent Reinforcement Learning
Authors: Hao Ma; Zhiqiang Pu; Yi Pan; Boyin Liu; Min Chen; Shijie Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Scalability remains a challenge in multi-agent reinforcement learning and is currently under active research. However, existing works lack the ability to identify the essential interaction under the non-stationary environment. We propose causal mean field Q-learning (CMFQ) to address this problem. It has the advantage of MFQ, which can compress the space size dramatically. Besides, it is ever more robust toward the non-stationary caused by increasing agents. We enable agents to identify which ally or opponent is more crucial by asking "what if" with the help of the structural causal model (SCM), then pay more attention to more crucial ones. We test CMFQ in mixed cooperative-competitive and cooperative games, which verify our method's scalability performance.
Design of the topology for contrastive visual-textual alignment
Authors: Zhun Sun
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Pre-training weakly related image-text pairs in the contrastive style shows great power in learning semantic aligning cross-modal models. The common choice to measure the distance between the feature representations of the image-text pairs is the cosine similarity, which can be considered as the negative inner product distance of features embedded on a sphere, mathematically. However, empirically, aligning image-text pairs on the spherical topology is vulnerable to the semantic ambiguity phenomenon resulting from the noise in the pre-training datasets. Specifically, under the noisy training data, instead of the optimal alignment-uniformity solution, the system would achieve an equilibrium (a gap between distances of positive and negative pairs), when the gradients for attraction and repulsion are neutralized.  Although intuitively, the model should always find this equilibrium given a sufficiently long training scheme, its numerical values might be out of the distance range (e.g. [-1, 1] for the cosine similarity). In the practice of former studies, this problem is partly tackled by introducing a learnable softmax temperature parameter, in other words, by explicitly scaling the range of the distance function.  In this work, we alternatively design the topology of embedding space and its endowed distance function. Motivated by studies that make use of Riemannian geometry for visual tasks, we propose a rather simple solution to address the aforementioned equilibrium problem. That is, we map the feature representations onto the oblique manifold endowed with the negative inner product as the distance function. In the experimental analysis, we show that we can improve the baseline performance by a large margin (e.g. 4\% in the zero-shot image to text retrieval task) by changing only two lines of the training codes.