402 citations · 923 across the 41 of their papers we have counts for
41 papers
The Sparsity Whisperer
Linghao Kong, Inimai Subramanian, Micah Adler +3
Pruning reduces the inference cost of large language models, but existing criteria primarily preserve large activations or reconstruct layer outputs. We argue that this overlooks a…
Toy Combinatorial Interpretability Models Reveal Lottery Tickets in Early Feature Space
Alon Bebchuk, Nir Shavit
The lottery ticket hypothesis posits that dense networks contain sparse subnetworks, ``winning tickets,'' that, when rewound to their initial weights and retrained in isolation, ma…
An Interpretable Latency Model for Speculative Decoding in LLM Serving
Linghao Kong, Megan Flynn, Michael Peng +3
Speculative decoding (SD) accelerates large language model (LLM) inference by using a smaller draft model to propose multiple tokens that are verified by a larger target model in p…
Understanding Empirical Unlearning with Combinatorial Interpretability
Shingo Kodama, Niv Cohen, Micah Adler +1
While many recent methods aim to unlearn or remove knowledge from pretrained models, seemingly erased knowledge often persists and can be recovered in various ways. Because large f…
Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination and Generation
Xuwang Yin, Claire Zhang, Julie Steele +2
Simultaneously achieving robust classification and high-fidelity generative modeling within a single framework presents a significant challenge. Hybrid approaches, such as Joint En…
Learning to Interpret Weight Differences in Language Models
Avichal Goel, Yoon Kim, Nir Shavit +1
Finetuning (pretrained) language models is a standard approach for updating their internal parametric knowledge and specializing them to new tasks and domains. However, the corresp…
Expand Neurons, Not Parameters
Linghao Kong, Inimai Subramanian, Yonadav Shavit +3
This work demonstrates how increasing the number of neurons in a network without increasing its total number of non-zero parameters improves performance. We show that this gain cor…
Budgeted Broadcast: An Activity-Dependent Pruning Rule for Neural Network Efficiency
Yaron Meirovitch, Fuming Yang, Jeff Lichtman +1
Most pruning methods remove parameters ranked by impact on loss (e.g., magnitude or gradient). We propose Budgeted Broadcast (BB), which gives each unit a local traffic budget (the…
Negative Pre-activations Differentiate Syntax
Linghao Kong, Angelina Ning, Micah Adler +1
Modern large language models increasingly use smooth activation functions such as GELU or SiLU, allowing negative pre-activations to carry both signal and gradient. Nevertheless, m…
The Birth of Knowledge: Emergent Features across Time, Space, and Scale in Large Language Models
Shashata Sawmya, Micah Adler, Nir Shavit
This paper studies the emergence of interpretable categorical features within large language models (LLMs), analyzing their behavior across training checkpoints (time), transformer…
Towards Combinatorial Interpretability of Neural Computation
Micah Adler, Dan Alistarh, Nir Shavit
We introduce combinatorial interpretability, a methodology for understanding neural computation by analyzing the combinatorial structures in the sign-based categorization of a netw…
Cascade Detector Analysis and Application to Biomedical Microscopy
Thomas L. Athey, Shashata Sawmya, Nir Shavit
As both computer vision models and biomedical datasets grow in size, there is an increasing need for efficient inference algorithms. We utilize cascade detectors to efficiently ide…
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic
Shashata Sawmya, Thomas L. Athey, Gwyneth Liu +1
Training segmentation models from scratch has been the standard approach for new electron microscopy connectomics datasets. However, leveraging pretrained models from existing data…
Jailbreak Defense in a Narrow Domain: Limitations of Existing Methods and a New Transcript-Classifier Approach
Tony T. Wang, John Hughes, Henry Sleight +7
Defending large language models against jailbreaks so that they never engage in a broadly-defined set of forbidden behaviors is an open problem. In this paper, we investigate the d…
On the Complexity of Neural Computation in Superposition
Micah Adler, Nir Shavit
Superposition, the ability of neural networks to represent more features than neurons, is increasingly seen as key to the efficiency of large models. This paper investigates the th…
Panza: Design and Analysis of a Fully-Local Personalized Text Writing Assistant
Armand Nicolicioiu, Eugenia Iofinova, Andrej Jovanovic +6
The availability of powerful open-source large language models (LLMs) opens exciting use-cases, such as using personal data to fine-tune these models to imitate a user's unique wri…
Wasserstein Distances, Neuronal Entanglement, and Sparsity
Shashata Sawmya, Linghao Kong, Ilia Markov +2
Disentangling polysemantic neurons is at the core of many current approaches to interpretability of large language models. Here we attempt to study how disentanglement can be used…
Forbidden Facts: An Investigation of Competing Objectives in Llama-2
Tony T. Wang, Miles Wang, Kaivalya Hariharan +1
LLMs often face competing pressures (for example helpfulness vs. harmlessness). To understand how models resolve such conflicts, we study Llama-2-chat models on the forbidden fact…
X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics
Yicong Li, Yaron Meirovitch, Aaron T. Kuan +5
Comprehensive, synapse-resolution imaging of the brain will be crucial for understanding neuronal computations and function. In connectomics, this has been the sole purview of volu…
The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons
Tri Nguyen, Mukul Narwani, Mark Larson +9
The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabl…
Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework
Lu Mi, Tianxing He, Core Francisco Park +3
Latent-space interpolation is commonly used to demonstrate the generalization ability of deep latent variable models. Various algorithms have been proposed to calculate the best tr…
HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps
Lu Mi, Hang Zhao, Charlie Nash +7
High Definition (HD) maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. They are critical for several key stages in an autonomous drivin…
Learning Guided Electron Microscopy with Active Acquisition
Lu Mi, Hao Wang, Yaron Meirovitch +5
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection. Datasets are typic…
On the Predictability of Pruning Across Scales
Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin +1
We show that the error of iteratively magnitude-pruned networks empirically follows a scaling law with interpretable coefficients that depend on the architecture and task. We funct…