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VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images
Hence, we consider asking a VLM to provide the scientific name of the organism shown in a given image. There are two types of questions that we consider for this task. First, we consider open-ended questions, where we do not provide any answer choices (or options) to the VLM in the input prompt.
Spectral Adapter: Fine-Tuning in Spectral Space
Recent developments in Parameter-Efficient Fine-Tuning (PEFT) methods for pre-trained deep neural networks have captured widespread interest.
B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory
We leverage ideas from Stochastic Realization Theory to develop a class of models called B'MOJO to seamlessly combine eidetic and fading memory within an elementary composable module. The overall architecture can be used to implement models that can access short-term eidetic memory "in-context," permanent structural memory "in-weights,"
Simple and Effective Masked Diffusion Language Models
MDLM's objective correspond to a principled variational lower bound, and supports generation via
Geometry-aware training of factorized layers in tensor Tucker format
Reducing parameter redundancies in neural network architectures is crucial for achieving feasible computational and memory requirements during training and inference phases.
